diff --git a/README.md b/README.md
index 7ef7812d..84185aa5 100644
--- a/README.md
+++ b/README.md
@@ -23,10 +23,10 @@ pip install qlasskit
 For a quickstart, read the _quickstart_ and _examples_ notebooks from the documentation: [https://dakk.github.io/qlasskit](https://dakk.github.io/qlasskit).
 
 ```python
-from qlasskit import qlassf, Qint4 
+from qlasskit import qlassf, Qint 
 
 @qlassf
-def h(k: Qint4) -> bool:
+def h(k: Qint[4]) -> bool:
     h = True
     for i in range(4):
         h = h and k[i]
@@ -65,11 +65,11 @@ You can also use other functions inside a qlassf:
 
 ```python
 @qlassf
-def equal_8(n: Qint4) -> bool:
+def equal_8(n: Qint[4]) -> bool:
   return equal_8 == 8
 
 @qlassfa(defs=[equal_8])
-def f(n: Qint4) -> bool:
+def f(n: Qint[4]) -> bool:
   n = n+1 if equal_8(n) else n
   return n
 ```
@@ -78,13 +78,13 @@ Qlasskit supports complex data types, like tuples and fixed size lists:
 
 ```python
 @qlassf
-def f(a: Tuple[Qint8, Qint8]) -> Tuple[bool, bool]:
+def f(a: Tuple[Qint[8], Qint[8]]) -> Tuple[bool, bool]:
   return a[0] == 42, a[1] == 0
 ```
 
 ```python
 @qlassf
-def search(alist: Qlist[Qint2, 4], to_search: Qint2):
+def search(alist: Qlist[Qint[2], 4], to_search: Qint[2]):
   for x in alist:
     if x == to_search:
       return True
diff --git a/docs/source/algorithms.ipynb b/docs/source/algorithms.ipynb
index 1c599561..2bf8d7f1 100644
--- a/docs/source/algorithms.ipynb
+++ b/docs/source/algorithms.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -41,18 +41,18 @@
        "4"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from typing import Tuple\n",
-    "from qlasskit import qlassf, Qint4\n",
+    "from qlasskit import qlassf, Qint, Qint4\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def test_tools(a: Qint4) -> Qint4:\n",
+    "def test_tools(a: Qint[4]) -> Qint[4]:\n",
     "    return a + 1\n",
     "\n",
     "\n",
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -77,7 +77,7 @@
        "'0100'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
     "\n",
     "\n",
     "@qlassf\n",
-    "def f(a: Qint4) -> Qint4:\n",
+    "def f(a: Qint[4]) -> Qint[4]:\n",
     "    return (a >> 3) + 1\n",
     "\n",
     "\n",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
     "\n",
     "\n",
     "@qlassf\n",
-    "def f(b: Qint4) -> bool:\n",
+    "def f(b: Qint[4]) -> bool:\n",
     "    return b < 16\n",
     "\n",
     "\n",
diff --git a/docs/source/bqm.ipynb b/docs/source/bqm.ipynb
index 7fa3566d..92893061 100644
--- a/docs/source/bqm.ipynb
+++ b/docs/source/bqm.ipynb
@@ -29,11 +29,11 @@
     }
    ],
    "source": [
-    "from qlasskit import qlassf, Qint4, Qint3, Parameter\n",
+    "from qlasskit import qlassf, Qint, Parameter\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def test_factor_generic(num: Parameter[Qint4], a: Qint3, b: Qint3) -> Qint4:\n",
+    "def test_factor_generic(num: Parameter[Qint[4]], a: Qint[3], b: Qint[3]) -> Qint[4]:\n",
     "    return num - (a * b)\n",
     "\n",
     "\n",
@@ -107,14 +107,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'a': 5, 'b': 3}\n"
+     "ename": "SolverFailureError",
+     "evalue": "Problem not accepted because user has insufficient remaining solver access time in project DEV.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mSolverFailureError\u001b[0m                        Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m sampler \u001b[38;5;241m=\u001b[39m EmbeddingComposite(DWaveSampler())\n\u001b[1;32m      4\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m sampler\u001b[38;5;241m.\u001b[39msample(bqm, num_reads\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m decoded_samples \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msampleset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m best_sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(decoded_samples, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39menergy)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(best_sample\u001b[38;5;241m.\u001b[39msample)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/bqm.py:142\u001b[0m, in \u001b[0;36mdecode_samples\u001b[0;34m(qf, sampleset)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get dimod sampleset and return an high level decoded solution\"\"\"\u001b[39;00m\n\u001b[1;32m    141\u001b[0m model \u001b[38;5;241m=\u001b[39m qf\u001b[38;5;241m.\u001b[39mto_bqm(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpq_model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 142\u001b[0m decoded \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43msampleset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m new_dec \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m el \u001b[38;5;129;01min\u001b[39;00m decoded:\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121\u001b[0m, in \u001b[0;36mSampleSet.record\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrecord\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1107\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\":obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.\u001b[39;00m\n\u001b[1;32m   1108\u001b[0m \n\u001b[1;32m   1109\u001b[0m \u001b[38;5;124;03m    Examples:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1119\u001b[0m \n\u001b[1;32m   1120\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1121\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1122\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_record\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m   1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m     samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1486\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m   1487\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/composites/embedding.py:284\u001b[0m, in \u001b[0;36mEmbeddingComposite.sample.<locals>.async_unembed\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21masync_unembed\u001b[39m(response):\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;66;03m# unembed the sampleset aysnchronously.\u001b[39;00m\n\u001b[1;32m    282\u001b[0m     warninghandler\u001b[38;5;241m.\u001b[39mchain_break(response, embedding)\n\u001b[0;32m--> 284\u001b[0m     sampleset \u001b[38;5;241m=\u001b[39m \u001b[43munembed_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_bqm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbqm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    285\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mchain_break_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain_break_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    286\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mchain_break_fraction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain_break_fraction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mreturn_embedding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_embedding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    289\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m return_embedding:\n\u001b[1;32m    290\u001b[0m         sampleset\u001b[38;5;241m.\u001b[39minfo[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124membedding_context\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m    291\u001b[0m             embedding_parameters\u001b[38;5;241m=\u001b[39membedding_parameters,\n\u001b[1;32m    292\u001b[0m             chain_strength\u001b[38;5;241m=\u001b[39membedding\u001b[38;5;241m.\u001b[39mchain_strength)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/embedding/transforms.py:606\u001b[0m, in \u001b[0;36munembed_sampleset\u001b[0;34m(target_sampleset, embedding, source_bqm, chain_break_method, chain_break_fraction, return_embedding)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    604\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgiven bqm does not match the embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 606\u001b[0m record \u001b[38;5;241m=\u001b[39m \u001b[43mtarget_sampleset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecord\u001b[49m\n\u001b[1;32m    608\u001b[0m unembedded, idxs \u001b[38;5;241m=\u001b[39m chain_break_method(target_sampleset, chains)\n\u001b[1;32m    610\u001b[0m reserved \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m'\u001b[39m}\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121\u001b[0m, in \u001b[0;36mSampleSet.record\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrecord\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1107\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\":obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.\u001b[39;00m\n\u001b[1;32m   1108\u001b[0m \n\u001b[1;32m   1109\u001b[0m \u001b[38;5;124;03m    Examples:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1119\u001b[0m \n\u001b[1;32m   1120\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1121\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1122\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_record\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m   1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m     samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1486\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m   1487\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:452\u001b[0m, in \u001b[0;36mDWaveSampler.sample.<locals>._hook\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SolverError, InvalidAPIResponseError) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfailover:\n\u001b[0;32m--> 452\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, SolverAuthenticationError):\n\u001b[1;32m    454\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:439\u001b[0m, in \u001b[0;36mDWaveSampler.sample.<locals>._hook\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sampleset\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomputation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProblemUploadError, RequestTimeout, PollingTimeout) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    442\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfailover:\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:429\u001b[0m, in \u001b[0;36mDWaveSampler.sample.<locals>._hook.<locals>.resolve\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresolve\u001b[39m(computation):\n\u001b[1;32m    428\u001b[0m     sampleset \u001b[38;5;241m=\u001b[39m computation\u001b[38;5;241m.\u001b[39msampleset\n\u001b[0;32m--> 429\u001b[0m     \u001b[43msampleset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m warninghandler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    432\u001b[0m         warninghandler\u001b[38;5;241m.\u001b[39mtoo_few_samples(sampleset)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m   1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m     samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1486\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m   1487\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:823\u001b[0m, in \u001b[0;36mFuture.sampleset.<locals>.<lambda>\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m    819\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt construct SampleSet without dimod. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    820\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-install the library with \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbqm\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m support.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    822\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampleset \u001b[38;5;241m=\u001b[39m sampleset \u001b[38;5;241m=\u001b[39m dimod\u001b[38;5;241m.\u001b[39mSampleSet\u001b[38;5;241m.\u001b[39mfrom_future(\n\u001b[0;32m--> 823\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mlambda\u001b[39;00m f: \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    825\u001b[0m \u001b[38;5;66;03m# propagate id to sampleset as well\u001b[39;00m\n\u001b[1;32m    826\u001b[0m \u001b[38;5;66;03m# note: this requires dimod>=0.8.21 (before that version SampleSet\u001b[39;00m\n\u001b[1;32m    827\u001b[0m \u001b[38;5;66;03m# had slots set which prevented dynamic addition of attributes).\u001b[39;00m\n\u001b[1;32m    828\u001b[0m sampleset\u001b[38;5;241m.\u001b[39mwait_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwait_id\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:755\u001b[0m, in \u001b[0;36mFuture.wait_sampleset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    752\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking sampleset getter.\"\"\"\u001b[39;00m\n\u001b[1;32m    754\u001b[0m \u001b[38;5;66;03m# blocking result get\u001b[39;00m\n\u001b[0;32m--> 755\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    757\u001b[0m \u001b[38;5;66;03m# common problem info: id/label\u001b[39;00m\n\u001b[1;32m    758\u001b[0m problem_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(problem_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:893\u001b[0m, in \u001b[0;36mFuture._load_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    891\u001b[0m \u001b[38;5;66;03m# Check for other error conditions\u001b[39;00m\n\u001b[1;32m    892\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 893\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    895\u001b[0m \u001b[38;5;66;03m# If someone else took care of this while we were waiting\u001b[39;00m\n\u001b[1;32m    896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/client/base.py:1309\u001b[0m, in \u001b[0;36mClient._handle_problem_status\u001b[0;34m(self, message, future)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_code\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m message \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_msg\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m message:\n\u001b[1;32m   1308\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError response received: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, message)\n\u001b[0;32m-> 1309\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m SolverFailureError(message[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_msg\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m   1311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m message:\n\u001b[1;32m   1312\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InvalidAPIResponseError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m missing in problem description response\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mSolverFailureError\u001b[0m: Problem not accepted because user has insufficient remaining solver access time in project DEV."
      ]
     }
    ],
diff --git a/docs/source/example_bqm_polynomial.ipynb b/docs/source/example_bqm_polynomial.ipynb
index 21ddad40..6f403bb5 100644
--- a/docs/source/example_bqm_polynomial.ipynb
+++ b/docs/source/example_bqm_polynomial.ipynb
@@ -38,14 +38,14 @@
     }
    ],
    "source": [
-    "from qlasskit import qlassf, qlassfa, Qmatrix, Qint2, Qint3, Qint4, Qfixed\n",
-    "from qlasskit.types.qfixed import Qfixed2_4\n",
+    "from qlasskit import qlassf, Qfixed\n",
     "\n",
     "\n",
     "@qlassf\n",
     "def poly(x: Qfixed[3, 3], y: Qfixed[3, 3]) -> Qfixed[3, 3]:\n",
     "    return x * 3 - y * 2 + 1\n",
     "\n",
+    "\n",
     "poly.expressions"
    ]
   },
@@ -70,14 +70,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'x': 7.875, 'y': 3.375} DecodedSample(-640.0, {'x': 7.875, 'y': 3.375})\n"
+      "{'x': 7.875, 'y': 7.375}\n"
      ]
     }
    ],
diff --git a/docs/source/example_bqm_tsp.ipynb b/docs/source/example_bqm_tsp.ipynb
index 501816f6..03775873 100644
--- a/docs/source/example_bqm_tsp.ipynb
+++ b/docs/source/example_bqm_tsp.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -12,23 +12,23 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 14\u001b[0m\n\u001b[1;32m     11\u001b[0m         dst_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m dst_matrix[oim][oi]\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dst_sum\n\u001b[0;32m---> 14\u001b[0m tsp_f \u001b[38;5;241m=\u001b[39m \u001b[43mtsp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbind\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdst_matrix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m tsp_f\u001b[38;5;241m.\u001b[39mexpressions\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# bqm = tsp_f.to_bqm()\u001b[39;00m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/qlassfun.py:105\u001b[0m, in \u001b[0;36mUnboundQlassf.bind\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moriginal_f is not available in python notebooks!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    103\u001b[0m     original_f \u001b[38;5;241m=\u001b[39m orig\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_translate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_ast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moriginal_f\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/qlassfun.py:285\u001b[0m, in \u001b[0;36mQlassF.from_function.<locals>._do_translate\u001b[0;34m(fun_ast, original_f)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_translate\u001b[39m(fun_ast, original_f\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 285\u001b[0m     fun \u001b[38;5;241m=\u001b[39m \u001b[43mast2ast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_ast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    286\u001b[0m     fun_name, args, fun_ret, exps \u001b[38;5;241m=\u001b[39m translate_ast(fun, types, defs)\n\u001b[1;32m    288\u001b[0m     exps \u001b[38;5;241m=\u001b[39m bool_optimizer\u001b[38;5;241m.\u001b[39mapply(exps)\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:495\u001b[0m, in \u001b[0;36mast2ast\u001b[0;34m(a_tree)\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mversion_info \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m9\u001b[39m):\n\u001b[1;32m    493\u001b[0m     a_tree \u001b[38;5;241m=\u001b[39m IndexReplacer()\u001b[38;5;241m.\u001b[39mvisit(a_tree)\n\u001b[0;32m--> 495\u001b[0m a_tree \u001b[38;5;241m=\u001b[39m \u001b[43mASTRewriter\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma_tree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;66;03m# print(ast.dump(a_tree))\u001b[39;00m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a_tree\n",
+      "Cell \u001b[0;32mIn[1], line 16\u001b[0m\n\u001b[1;32m     12\u001b[0m         dst_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m dst_matrix[oim][oi]\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dst_sum\n\u001b[0;32m---> 16\u001b[0m tsp_f \u001b[38;5;241m=\u001b[39m \u001b[43mtsp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbind\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdst_matrix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m tsp_f\u001b[38;5;241m.\u001b[39mexpressions\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# bqm = tsp_f.to_bqm()\u001b[39;00m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/qlassfun.py:110\u001b[0m, in \u001b[0;36mUnboundQlassf.bind\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moriginal_f is not available in python notebooks!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    108\u001b[0m     original_f \u001b[38;5;241m=\u001b[39m orig\n\u001b[0;32m--> 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_translate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_ast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moriginal_f\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/qlassfun.py:294\u001b[0m, in \u001b[0;36mQlassF.from_function.<locals>._do_translate\u001b[0;34m(fun_ast, original_f)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_translate\u001b[39m(fun_ast, original_f):\n\u001b[0;32m--> 294\u001b[0m     fun \u001b[38;5;241m=\u001b[39m \u001b[43mast2ast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_ast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    295\u001b[0m     fun_name, args, fun_ret, exps \u001b[38;5;241m=\u001b[39m translate_ast(fun, types, defs)\n\u001b[1;32m    297\u001b[0m     exps \u001b[38;5;241m=\u001b[39m bool_optimizer\u001b[38;5;241m.\u001b[39mapply(exps)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:546\u001b[0m, in \u001b[0;36mast2ast\u001b[0;34m(a_tree)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mversion_info \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m9\u001b[39m):\n\u001b[1;32m    544\u001b[0m     a_tree \u001b[38;5;241m=\u001b[39m IndexReplacer()\u001b[38;5;241m.\u001b[39mvisit(a_tree)\n\u001b[0;32m--> 546\u001b[0m a_tree \u001b[38;5;241m=\u001b[39m \u001b[43mASTRewriter\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma_tree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    547\u001b[0m \u001b[38;5;66;03m# print(ast.dump(a_tree))\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a_tree\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:418\u001b[0m, in \u001b[0;36mNodeVisitor.visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    416\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m node\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    417\u001b[0m visitor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneric_visit)\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:253\u001b[0m, in \u001b[0;36mASTRewriter.visit_FunctionDef\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    250\u001b[0m node\u001b[38;5;241m.\u001b[39mreturns \u001b[38;5;241m=\u001b[39m _replace_types_annotations(node\u001b[38;5;241m.\u001b[39mreturns)\n\u001b[1;32m    251\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mret \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mreturns\n\u001b[0;32m--> 253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeneric_visit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:282\u001b[0m, in \u001b[0;36mASTRewriter.visit_FunctionDef\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    279\u001b[0m node\u001b[38;5;241m.\u001b[39mreturns \u001b[38;5;241m=\u001b[39m _replace_types_annotations(node\u001b[38;5;241m.\u001b[39mreturns)\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mret \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mreturns\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeneric_visit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:494\u001b[0m, in \u001b[0;36mNodeTransformer.generic_visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m value \u001b[38;5;129;01min\u001b[39;00m old_value:\n\u001b[1;32m    493\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, AST):\n\u001b[0;32m--> 494\u001b[0m         value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    495\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    496\u001b[0m             \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:418\u001b[0m, in \u001b[0;36mNodeVisitor.visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    416\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m node\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    417\u001b[0m visitor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneric_visit)\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:400\u001b[0m, in \u001b[0;36mASTRewriter.visit_For\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    394\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([tar_assign]))\n\u001b[1;32m    396\u001b[0m     new_body \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    397\u001b[0m         NameValReplacer(node\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid, _val)\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b))\n\u001b[1;32m    398\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m    399\u001b[0m     ]\n\u001b[0;32m--> 400\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b)) \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m new_body]))\n\u001b[1;32m    402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rolls\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:400\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    394\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([tar_assign]))\n\u001b[1;32m    396\u001b[0m     new_body \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    397\u001b[0m         NameValReplacer(node\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid, _val)\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b))\n\u001b[1;32m    398\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m    399\u001b[0m     ]\n\u001b[0;32m--> 400\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m new_body]))\n\u001b[1;32m    402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rolls\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:433\u001b[0m, in \u001b[0;36mASTRewriter.visit_For\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    427\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([tar_assign]))\n\u001b[1;32m    429\u001b[0m     new_body \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    430\u001b[0m         NameValReplacer(node\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid, _val)\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b))\n\u001b[1;32m    431\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m    432\u001b[0m     ]\n\u001b[0;32m--> 433\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b)) \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m new_body]))\n\u001b[1;32m    435\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rolls\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:433\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    427\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([tar_assign]))\n\u001b[1;32m    429\u001b[0m     new_body \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    430\u001b[0m         NameValReplacer(node\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid, _val)\u001b[38;5;241m.\u001b[39mvisit(copy\u001b[38;5;241m.\u001b[39mdeepcopy(b))\n\u001b[1;32m    431\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m    432\u001b[0m     ]\n\u001b[0;32m--> 433\u001b[0m     rolls\u001b[38;5;241m.\u001b[39mextend(flatten([\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m new_body]))\n\u001b[1;32m    435\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rolls\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:418\u001b[0m, in \u001b[0;36mNodeVisitor.visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    416\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m node\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    417\u001b[0m visitor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneric_visit)\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:328\u001b[0m, in \u001b[0;36mASTRewriter.visit_AugAssign\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;66;03m# Reassigning an already present variable (use a temp variable)\u001b[39;00m\n\u001b[1;32m    322\u001b[0m \u001b[38;5;66;03m# if node.target.id in self.env:\u001b[39;00m\n\u001b[1;32m    323\u001b[0m new_targ \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mName(\u001b[38;5;28mid\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnode\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, ctx\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mLoad())\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m    326\u001b[0m     ast\u001b[38;5;241m.\u001b[39mAssign(\n\u001b[1;32m    327\u001b[0m         targets\u001b[38;5;241m=\u001b[39m[new_targ],\n\u001b[0;32m--> 328\u001b[0m         value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m            \u001b[49m\u001b[43mast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBinOp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    331\u001b[0m     ),\n\u001b[1;32m    332\u001b[0m     ast\u001b[38;5;241m.\u001b[39mAssign(\n\u001b[1;32m    333\u001b[0m         targets\u001b[38;5;241m=\u001b[39m[node\u001b[38;5;241m.\u001b[39mtarget],\n\u001b[1;32m    334\u001b[0m         value\u001b[38;5;241m=\u001b[39mnew_targ,\n\u001b[1;32m    335\u001b[0m     ),\n\u001b[1;32m    336\u001b[0m ]\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:359\u001b[0m, in \u001b[0;36mASTRewriter.visit_AugAssign\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;66;03m# Reassigning an already present variable (use a temp variable)\u001b[39;00m\n\u001b[1;32m    353\u001b[0m \u001b[38;5;66;03m# if node.target.id in self.env:\u001b[39;00m\n\u001b[1;32m    354\u001b[0m new_targ \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mName(\u001b[38;5;28mid\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnode\u001b[38;5;241m.\u001b[39mtarget\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, ctx\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mLoad())\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m    357\u001b[0m     ast\u001b[38;5;241m.\u001b[39mAssign(\n\u001b[1;32m    358\u001b[0m         targets\u001b[38;5;241m=\u001b[39m[new_targ],\n\u001b[0;32m--> 359\u001b[0m         value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m            \u001b[49m\u001b[43mast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBinOp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    361\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    362\u001b[0m     ),\n\u001b[1;32m    363\u001b[0m     ast\u001b[38;5;241m.\u001b[39mAssign(\n\u001b[1;32m    364\u001b[0m         targets\u001b[38;5;241m=\u001b[39m[node\u001b[38;5;241m.\u001b[39mtarget],\n\u001b[1;32m    365\u001b[0m         value\u001b[38;5;241m=\u001b[39mnew_targ,\n\u001b[1;32m    366\u001b[0m     ),\n\u001b[1;32m    367\u001b[0m ]\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:418\u001b[0m, in \u001b[0;36mNodeVisitor.visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    416\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m node\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    417\u001b[0m visitor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneric_visit)\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:118\u001b[0m, in \u001b[0;36mASTRewriter.generic_visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgeneric_visit\u001b[39m(\u001b[38;5;28mself\u001b[39m, node):\n\u001b[0;32m--> 118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeneric_visit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:143\u001b[0m, in \u001b[0;36mASTRewriter.generic_visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgeneric_visit\u001b[39m(\u001b[38;5;28mself\u001b[39m, node):\n\u001b[0;32m--> 143\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeneric_visit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:503\u001b[0m, in \u001b[0;36mNodeTransformer.generic_visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    501\u001b[0m     old_value[:] \u001b[38;5;241m=\u001b[39m new_values\n\u001b[1;32m    502\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(old_value, AST):\n\u001b[0;32m--> 503\u001b[0m     new_node \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    504\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m new_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    505\u001b[0m         \u001b[38;5;28mdelattr\u001b[39m(node, field)\n",
       "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/ast.py:418\u001b[0m, in \u001b[0;36mNodeVisitor.visit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    416\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvisit_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m node\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m    417\u001b[0m visitor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneric_visit)\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.15-py3.10.egg/qlasskit/ast2ast.py:139\u001b[0m, in \u001b[0;36mASTRewriter.visit_Subscript\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    136\u001b[0m     tup \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvalue\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tup, ast\u001b[38;5;241m.\u001b[39mTuple):\n\u001b[0;32m--> 139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot a tuple in ast2ast visit subscript with not constant _sval: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    141\u001b[0m         ast\u001b[38;5;241m.\u001b[39mdump(tup)\n\u001b[1;32m    142\u001b[0m     )\n\u001b[1;32m    144\u001b[0m elts \u001b[38;5;241m=\u001b[39m tup\u001b[38;5;241m.\u001b[39melts\n\u001b[1;32m    146\u001b[0m ifex \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mIfExp(\n\u001b[1;32m    147\u001b[0m     test\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mCompare(\n\u001b[1;32m    148\u001b[0m         left\u001b[38;5;241m=\u001b[39m_sval, ops\u001b[38;5;241m=\u001b[39m[ast\u001b[38;5;241m.\u001b[39mEq()], comparators\u001b[38;5;241m=\u001b[39m[ast\u001b[38;5;241m.\u001b[39mConstant(value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)]\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    151\u001b[0m     orelse\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mConstant(value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m),\n\u001b[1;32m    152\u001b[0m )\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/ast2ast.py:165\u001b[0m, in \u001b[0;36mASTRewriter.visit_Subscript\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    162\u001b[0m     tup \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvalue\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tup, ast\u001b[38;5;241m.\u001b[39mTuple):\n\u001b[0;32m--> 165\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[1;32m    166\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot a tuple in ast2ast visit subscript with not constant _sval: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    167\u001b[0m         \u001b[38;5;241m+\u001b[39m ast\u001b[38;5;241m.\u001b[39mdump(tup)\n\u001b[1;32m    168\u001b[0m     )\n\u001b[1;32m    170\u001b[0m elts \u001b[38;5;241m=\u001b[39m tup\u001b[38;5;241m.\u001b[39melts\n\u001b[1;32m    172\u001b[0m ifex \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mIfExp(\n\u001b[1;32m    173\u001b[0m     test\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mCompare(\n\u001b[1;32m    174\u001b[0m         left\u001b[38;5;241m=\u001b[39m_sval, ops\u001b[38;5;241m=\u001b[39m[ast\u001b[38;5;241m.\u001b[39mEq()], comparators\u001b[38;5;241m=\u001b[39m[ast\u001b[38;5;241m.\u001b[39mConstant(value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)]\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    177\u001b[0m     orelse\u001b[38;5;241m=\u001b[39mast\u001b[38;5;241m.\u001b[39mConstant(value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m),\n\u001b[1;32m    178\u001b[0m )\n",
       "\u001b[0;31mException\u001b[0m: Not a tuple in ast2ast visit subscript with not constant _sval: Subscript(value=Name(id='dst_matrix', ctx=Load()), slice=Name(id='oim', ctx=Load()), ctx=Load())"
      ]
     }
@@ -36,11 +36,13 @@
    "source": [
     "# Encode the TSP using qlasskit and parameters\n",
     "\n",
-    "from qlasskit import qlassf, Parameter, Qlist, Qmatrix, Qint2, Qint3, Qint4\n",
+    "from qlasskit import qlassf, Parameter, Qlist, Qmatrix, Qint\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def tsp(dst_matrix: Parameter[Qmatrix[Qint3, 3, 3]], order: Qlist[Qint2, 3]) -> Qint4:\n",
+    "def tsp(\n",
+    "    dst_matrix: Parameter[Qmatrix[Qint[3], 3, 3]], order: Qlist[Qint[2], 3]\n",
+    ") -> Qint[4]:\n",
     "    dst_sum = Qint4(0)\n",
     "    for i in range(2):\n",
     "        oim = order[i]\n",
diff --git a/docs/source/example_deutsch_jozsa.ipynb b/docs/source/example_deutsch_jozsa.ipynb
index cbb83460..1602089c 100644
--- a/docs/source/example_deutsch_jozsa.ipynb
+++ b/docs/source/example_deutsch_jozsa.ipynb
@@ -13,11 +13,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint4\n",
+    "from qlasskit import qlassf, Qint\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def f(a: Qint4) -> bool:\n",
+    "def f(a: Qint[4]) -> bool:\n",
     "    return a > 7"
    ]
   },
diff --git a/docs/source/example_grover.ipynb b/docs/source/example_grover.ipynb
index aa416ec2..c5c98542 100644
--- a/docs/source/example_grover.ipynb
+++ b/docs/source/example_grover.ipynb
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qlist, Qint2\n",
+    "from qlasskit import qlassf, Qlist\n",
     "\n",
     "\n",
     "@qlassf\n",
@@ -118,7 +118,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
diff --git a/docs/source/example_grover_factors.ipynb b/docs/source/example_grover_factors.ipynb
index 5809def1..bfeabb2c 100644
--- a/docs/source/example_grover_factors.ipynb
+++ b/docs/source/example_grover_factors.ipynb
@@ -9,17 +9,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Tuple\n",
-    "from qlasskit import qlassf, Qint2\n",
+    "from qlasskit import qlassf, Qint\n",
     "from qlasskit.algorithms import Grover\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def factorize(a: Tuple[Qint2, Qint2]) -> bool:\n",
+    "def factorize(a: Tuple[Qint[2], Qint[2]]) -> bool:\n",
     "    return a[0] * a[1] == 9\n",
     "\n",
     "\n",
@@ -28,17 +28,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -60,17 +60,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2252.61x3294.28 with 1 Axes>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/source/example_grover_hash.ipynb b/docs/source/example_grover_hash.ipynb
index c22230ae..440e7dfb 100644
--- a/docs/source/example_grover_hash.ipynb
+++ b/docs/source/example_grover_hash.ipynb
@@ -17,11 +17,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint4, Qint8, Qlist\n",
+    "from qlasskit import qlassf, Qint, Qint8, Qlist\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def hash_simp(m: Qlist[Qint4, 2]) -> Qint8:\n",
+    "def hash_simp(m: Qlist[Qint[4], 2]) -> Qint[8]:\n",
     "    hv = 0\n",
     "    for i in m:\n",
     "        hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xFF\n",
@@ -118,7 +118,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
diff --git a/docs/source/example_grover_subset.ipynb b/docs/source/example_grover_subset.ipynb
index d3c34102..0e22487e 100644
--- a/docs/source/example_grover_subset.ipynb
+++ b/docs/source/example_grover_subset.ipynb
@@ -11,16 +11,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint2, Qint3\n",
+    "from qlasskit import qlassf, Qint, Qint3\n",
     "from typing import Tuple\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def subset_sum(ii: Tuple[Qint2, Qint2]) -> Qint3:\n",
+    "def subset_sum(ii: Tuple[Qint[2], Qint[2]]) -> Qint[3]:\n",
     "    l = [0, 5, 2, 3]\n",
     "    return l[ii[0]] + l[ii[1]] if ii[0] != ii[1] else 0"
    ]
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,17 +54,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/source/example_grover_sudoku.ipynb b/docs/source/example_grover_sudoku.ipynb
index 52597f04..2b67a4e1 100644
--- a/docs/source/example_grover_sudoku.ipynb
+++ b/docs/source/example_grover_sudoku.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,17 +46,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -131,12 +131,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate `sudoku_check` using `Qint2` and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities."
+    "We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate `sudoku_check` using `Qint[2]` and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -148,11 +148,11 @@
     }
    ],
    "source": [
-    "from qlasskit import Qint2, Qint4\n",
+    "from qlasskit import Qint, Qint4\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def sudoku_check(m: Qmatrix[Qint2, 4, 4]) -> bool:\n",
+    "def sudoku_check(m: Qmatrix[Qint[2], 4, 4]) -> bool:\n",
     "    res = True\n",
     "\n",
     "    # Constraints\n",
diff --git a/docs/source/example_simon.ipynb b/docs/source/example_simon.ipynb
index 05728d9b..580fe040 100644
--- a/docs/source/example_simon.ipynb
+++ b/docs/source/example_simon.ipynb
@@ -9,21 +9,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint4\n",
+    "from qlasskit import qlassf, Qint\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def f(a: Qint4) -> Qint4:\n",
+    "def f(a: Qint[4]) -> Qint[4]:\n",
     "    return (a >> 3) + 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -33,7 +33,7 @@
        "<Figure size 371.107x535.111 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
        "<Figure size 789.163x535.111 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,17 +77,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/source/exporter.ipynb b/docs/source/exporter.ipynb
index dd5202e3..310a9390 100644
--- a/docs/source/exporter.ipynb
+++ b/docs/source/exporter.ipynb
@@ -20,11 +20,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import Qint2, qlassf\n",
+    "from qlasskit import Qint, qlassf\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def hello_world(a: bool, b: Qint2) -> Qint2:\n",
+    "def hello_world(a: bool, b: Qint[2]) -> Qint[2]:\n",
     "    return b + (1 if a else 0)"
    ]
   },
@@ -213,18 +213,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Gate(hello_world, targets=[0, 1, 2, 3, 4], controls=None, classical controls=None, control_value=None, classical_control_value=None)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Disabled on docs for a depencency problem\n",
     "# qc = hello_world.export(\"qutip\")\n",
diff --git a/docs/source/how_it_works.ipynb b/docs/source/how_it_works.ipynb
index fe3b9699..fdf53fa1 100644
--- a/docs/source/how_it_works.ipynb
+++ b/docs/source/how_it_works.ipynb
@@ -28,12 +28,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint2, Qint4\n",
+    "from qlasskit import qlassf\n",
     "from qiskit import QuantumCircuit\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def f_comp(b: bool, n: Qint2) -> Qint2:\n",
+    "def f_comp(b: bool, n: Qint[2]) -> Qint[2]:\n",
     "    for i in range(3):\n",
     "        n += 1 if b else 2\n",
     "    return n"
@@ -72,7 +72,7 @@
    ],
    "source": [
     "@qlassf\n",
-    "def f1(b: bool, n: Qint2) -> Qint2:\n",
+    "def f1(b: bool, n: Qint[2]) -> Qint[2]:\n",
     "    return n + (1 if b else 2)\n",
     "\n",
     "\n",
@@ -151,7 +151,7 @@
    "outputs": [],
    "source": [
     "@qlassf\n",
-    "def f(n: Qint4) -> bool:\n",
+    "def f(n: Qint[4]) -> bool:\n",
     "    return n == 3"
    ]
   },
diff --git a/docs/source/parameters.ipynb b/docs/source/parameters.ipynb
index 5981a698..0e3767b6 100644
--- a/docs/source/parameters.ipynb
+++ b/docs/source/parameters.ipynb
@@ -67,11 +67,11 @@
     }
    ],
    "source": [
-    "from qlasskit import Qlist, Qint2, Qint4\n",
+    "from qlasskit import Qlist, Qint, Qint4\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def test(a: Parameter[Qlist[Qint2, 4]], b: Qint4) -> Qint4:\n",
+    "def test(a: Parameter[Qlist[Qint[2], 4]], b: Qint[4]) -> Qint[4]:\n",
     "    s = Qint4(0)\n",
     "    for n in a:\n",
     "        s += n\n",
diff --git a/docs/source/quickstart.ipynb b/docs/source/quickstart.ipynb
index f1fe0361..271207da 100644
--- a/docs/source/quickstart.ipynb
+++ b/docs/source/quickstart.ipynb
@@ -19,11 +19,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qlasskit import qlassf, Qint2\n",
+    "from qlasskit import qlassf, Qint, Qint2\n",
     "\n",
     "\n",
     "@qlassf\n",
-    "def sum_two_numbers(a: Qint2, b: Qint2) -> Qint2:\n",
+    "def sum_two_numbers(a: Qint[2], b: Qint[2]) -> Qint[2]:\n",
     "    return a + b"
    ]
   },
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
        "<Figure size 789.163x618.722 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +105,7 @@
        "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +136,15 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
    "version": "3.10.13"
   }
  },
diff --git a/docs/source/supported.rst b/docs/source/supported.rst
index 1375daea..dc9ce1af 100644
--- a/docs/source/supported.rst
+++ b/docs/source/supported.rst
@@ -29,14 +29,15 @@ Boolean type.
 Qint
 ^^^^
 
-Unsigned integers; this type has subtypes for different Qint sizes (Qint2, Qint4, Qint8, Qint12, Qint16). 
+Unsigned integers; `Qint[2]` has 2 bits, and there other sizes are supported.
 Single bit of the Qint are accessible by the subscript operator `[]`.
-
+All the supported sizes have a constructor `Qintn()` defined in `qlasskit.types`.
 
 Qfixed
 ^^^^^^
 
 Fixed point rational number; `Qfixed[2,3]` has 2 bits for the integer part and 3 bits for the fractional.
+All the supported sizes have a constructor `Qfixedn_m()` defined in `qlasskit.types`.
 
 Qchar
 ^^^^^
@@ -53,14 +54,14 @@ List
 ^^^^
 
 Qlist[T, size] denotes a fixed-size list in qlasskit. 
-For example, the list `[1,2,3]` is typed as `Qlist[Qint2,3]`.
+For example, the list `[1,2,3]` is typed as `Qlist[Qint[2],3]`.
 
 
 Matrix
 ^^^^
 
 Qmatrix[T, m, n] denotes a fixed-size list in qlasskit. 
-For example, the matrix `[[1,2],[3,4]]` is typed as `Qmatrix[Qint2,2,2]`.
+For example, the matrix `[[1,2],[3,4]]` is typed as `Qmatrix[Qint[2],2,2]`.
 
 
 
@@ -243,7 +244,7 @@ Function def
 
 .. code-block:: python
 
-   def f(t: Qlist[Qint4,2]) -> Qint4:
+   def f(t: Qlist[Qint[4],2]) -> Qint[4]:
       return t[0] + t[1]
 
 
diff --git a/qlasskit/ast2logic/t_arguments.py b/qlasskit/ast2logic/t_arguments.py
index 2c153f55..d068c979 100644
--- a/qlasskit/ast2logic/t_arguments.py
+++ b/qlasskit/ast2logic/t_arguments.py
@@ -25,7 +25,10 @@ def to_name(a):
         if isinstance(a, ast.Attribute):
             return a.attr
         elif isinstance(a, ast.Subscript):  # for Qfixed and similar
-            t = "_".join(f"{e.value}" for e in a.slice.elts)
+            if isinstance(a.slice, ast.Constant):
+                t = f"{a.slice.value}"
+            else:
+                t = "_".join(f"{e.value}" for e in a.slice.elts)
             return f"{a.value.id}{t}"
         else:
             return a.id
diff --git a/qlasskit/types/__init__.py b/qlasskit/types/__init__.py
index 411d7fca..a12de173 100644
--- a/qlasskit/types/__init__.py
+++ b/qlasskit/types/__init__.py
@@ -43,13 +43,35 @@ def _full_adder(c, a, b):  # Carry x Sum
     # return c, o
 
 
-from .qtype import Qtype, TExp, TType  # noqa: F401, E402
+from .qtype import (  # noqa: F401, E402
+    Qtype,
+    TExp,
+    TType,
+    bin_to_bool_list,
+    bool_list_to_bin,
+)
 from .qbool import Qbool  # noqa: F401, E402
 from .qlist import Qlist  # noqa: F401, E402
 
 from .qmatrix import Qmatrix  # noqa: F401, E402
 from .qchar import Qchar  # noqa: F401, E402
-from .qfixed import Qfixed, QFIXED_TYPES  # noqa: F401, E402
+from .qfixed import (  # noqa: F401, E402
+    Qfixed,
+    Qfixed1_2,
+    Qfixed1_3,
+    Qfixed1_4,
+    Qfixed1_6,
+    Qfixed2_2,
+    Qfixed2_3,
+    Qfixed2_4,
+    Qfixed2_6,
+    Qfixed3_3,
+    Qfixed3_4,
+    Qfixed3_6,
+    Qfixed4_4,
+    Qfixed4_6,
+    QFIXED_TYPES,
+)
 from .qint import (  # noqa: F401, E402
     Qint,
     Qint2,
@@ -61,29 +83,25 @@ def _full_adder(c, a, b):  # Carry x Sum
     Qint8,
     Qint12,
     Qint16,
+    QINT_TYPES,
 )
 from .parameter import Parameter  # noqa: F401, E402
 
-BUILTIN_TYPES = [
-    Qint2,
-    Qint3,
-    Qint4,
-    Qint5,
-    Qint6,
-    Qint7,
-    Qint8,
-    Qint12,
-    Qint16,
-    Qchar,
-    Qlist,
-    Qmatrix,
-    Qfixed,
-] + QFIXED_TYPES
+BUILTIN_TYPES = (
+    [
+        Qchar,
+        Qlist,
+        Qmatrix,
+        Qfixed,
+    ]
+    + QINT_TYPES
+    + QFIXED_TYPES
+)
 
 
 def const_to_qtype(value: Any) -> TExp:
     if isinstance(value, int):
-        for det_type in [Qint2, Qint4, Qint6, Qint8, Qint12, Qint16]:  # Qint3, Qint5
+        for det_type in [Qint2, Qint4, Qint6, Qint8, Qint12, Qint16]:  # QINT_TYPES?
             if value < 2**det_type.BIT_SIZE:
                 return det_type.const(value)
 
diff --git a/qlasskit/types/qchar.py b/qlasskit/types/qchar.py
index 7531b610..a636ebb7 100644
--- a/qlasskit/types/qchar.py
+++ b/qlasskit/types/qchar.py
@@ -17,7 +17,7 @@
 from sympy.logic import And, Or, false, true
 
 from . import _eq, _neq
-from .qint import Qint
+from .qint import QintImp
 from .qtype import Qtype, TExp, bin_to_bool_list, bool_list_to_bin
 
 
@@ -43,7 +43,7 @@ def from_bool(cls, v: List[bool]):
 
     @classmethod
     def comparable(cls, other_type=None) -> bool:
-        return other_type == cls or issubclass(other_type, Qint)
+        return other_type == cls or issubclass(other_type, QintImp)
 
     @classmethod
     def const(cls, value: Any) -> TExp:
diff --git a/qlasskit/types/qfixed.py b/qlasskit/types/qfixed.py
index 6df75eca..149e150a 100644
--- a/qlasskit/types/qfixed.py
+++ b/qlasskit/types/qfixed.py
@@ -18,13 +18,13 @@
 from sympy.logic import And, Not, Or, false, true
 
 from . import _eq, _neq
-from .qint import Qint
+from .qint import QintImp
 from .qtype import Qtype, TExp, bin_to_bool_list, bool_list_to_bin
 
 
 class QfixedImp(float, Qtype):
     """Implementation of the Qfixed type
-    A number i.f is encoded in a Qfixed_2_4 as iiffff.
+    A number i.f is encoded in a Qfixed2_4 as iiffff.
     The interger part is in little endian like Qint
     Fractional part is obtained as binary fractions, by multiplying for
     2 and getting the integer part at each step.
@@ -97,7 +97,7 @@ def to_amplitudes(self) -> List[float]:
     def comparable(cls, other_type=None) -> bool:
         return (
             other_type == cls
-            or issubclass(other_type, Qint)
+            or issubclass(other_type, QintImp)
             or issubclass(other_type, QfixedImp)
         )
 
@@ -200,7 +200,7 @@ def add(cls, tleft: TExp, tright: TExp) -> TExp:
         tl_v = QfixedImp._to_qint_repr(tleft)
         tr_v = QfixedImp._to_qint_repr(tright)
 
-        res = Qint.add((tleft[0], tl_v), (tright[0], tr_v))
+        res = QintImp.add((tleft[0], tl_v), (tright[0], tr_v))
 
         return (tleft[0], QfixedImp._from_qint_repr((tleft[0], res[1])))
 
@@ -216,10 +216,10 @@ def mul(cls, tleft: TExp, tright: TExp) -> TExp:  # noqa: C901
         a = len(list(filter(lambda b: b is bool, tleft[1])))
         b = len(list(filter(lambda b: b is bool, tright[1])))
 
-        if a == 0 and issubclass(tleft[0], Qint):  # type: ignore
+        if a == 0 and issubclass(tleft[0], QintImp):  # type: ignore
             tconst = tleft
             top = tright
-        elif b == 0 and issubclass(tright[0], Qint):  # type: ignore
+        elif b == 0 and issubclass(tright[0], QintImp):  # type: ignore
             top = tleft
             tconst = tright
         else:
@@ -333,13 +333,31 @@ class Qfixed4_6(QfixedImp):
     Qfixed4_6,
 ]
 
+# class _GetQfixedTypeInner:
+#     def __init__(self, base):
+#         self.base = base
+
+#     def __getitem__(self, index):
+#         return eval(f"Qfixed{self.base}_{index}")
+
+# class _GetQfixedType:
+#     @property
+#     def __name__(self):
+#         return 'Qfixed' # I'm not sure this is correct
+
+#     def __getitem__(self, index):
+#         return _GetQfixedTypeInner(index)
+
 
 class QfixedMeta(type):
     def __getitem__(cls, params):
         if isinstance(params, tuple) and len(params) == 2:
             i, f = params
             if isinstance(i, int) and isinstance(f, int) and i >= 0 and f >= 0:
-                return f"Qfixed{i}_{f}"  # TODO: transform to type
+                return f"Qfixed{i}_{f}"
+
+    # def __new__(cls, name, bases, dct):
+    #     return _GetQfixedType()
 
 
 class Qfixed(metaclass=QfixedMeta):
diff --git a/qlasskit/types/qint.py b/qlasskit/types/qint.py
index 4ee521d6..2572cebf 100644
--- a/qlasskit/types/qint.py
+++ b/qlasskit/types/qint.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Davide Gessa
+# Copyright 2023-2024 Davide Gessa
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@
 from .qtype import Qtype, TExp, bin_to_bool_list, bool_list_to_bin
 
 
-class Qint(int, Qtype):
+class QintImp(int, Qtype):
     BIT_SIZE = 8
 
     def __init__(self, value):
@@ -50,7 +50,7 @@ def to_amplitudes(self) -> List[float]:
     def comparable(cls, other_type=None) -> bool:
         """Return true if the type is comparable with itself or
         with [other_type]"""
-        if not other_type or issubclass(other_type, Qint):
+        if not other_type or issubclass(other_type, QintImp):
             return True
         return False
 
@@ -122,17 +122,20 @@ def gt(tleft: TExp, tcomp: TExp) -> TExp:
     @staticmethod
     def lt(tleft: TExp, tcomp: TExp) -> TExp:
         """Compare two Qint for lower than"""
-        return (bool, And(Not(Qint.gt(tleft, tcomp)[1]), Not(Qint.eq(tleft, tcomp)[1])))
+        return (
+            bool,
+            And(Not(QintImp.gt(tleft, tcomp)[1]), Not(QintImp.eq(tleft, tcomp)[1])),
+        )
 
     @staticmethod
     def lte(tleft: TExp, tcomp: TExp) -> TExp:
         """Compare two Qint for lower than - equal"""
-        return (bool, Not(Qint.gt(tleft, tcomp)[1]))
+        return (bool, Not(QintImp.gt(tleft, tcomp)[1]))
 
     @staticmethod
     def gte(tleft: TExp, tcomp: TExp) -> TExp:
         """Compare two Qint for greater than - equal"""
-        return (bool, Not(Qint.lt(tleft, tcomp)[1]))
+        return (bool, Not(QintImp.lt(tleft, tcomp)[1]))
 
     # Operations
 
@@ -234,40 +237,63 @@ def bitwise_or(cls, tleft: TExp, tright: TExp) -> TExp:
         return cls.bitwise_generic(Or, tleft, tright)
 
 
-class Qint2(Qint):
+class Qint2(QintImp):
     BIT_SIZE = 2
 
 
-class Qint3(Qint):
+class Qint3(QintImp):
     BIT_SIZE = 3
 
 
-class Qint4(Qint):
+class Qint4(QintImp):
     BIT_SIZE = 4
 
 
-class Qint5(Qint):
+class Qint5(QintImp):
     BIT_SIZE = 5
 
 
-class Qint6(Qint):
+class Qint6(QintImp):
     BIT_SIZE = 6
 
 
-class Qint7(Qint):
+class Qint7(QintImp):
     BIT_SIZE = 7
 
 
-class Qint8(Qint):
+class Qint8(QintImp):
     BIT_SIZE = 8
 
 
-class Qint12(Qint):
+class Qint12(QintImp):
     BIT_SIZE = 12
 
 
-class Qint16(Qint):
+class Qint16(QintImp):
     BIT_SIZE = 16
 
 
 QINT_TYPES = [Qint2, Qint3, Qint4, Qint5, Qint6, Qint7, Qint8, Qint12, Qint16]
+
+# class _GetQintType:
+#     @property
+#     def __name__(self):
+#         return 'Qint' # I'm not sure this is correct
+
+#     def __getitem__(self, index):
+#         return eval(f"Qint{index}")
+
+
+class QintMeta(type):
+    def __getitem__(cls, params):
+        if isinstance(params, tuple) and len(params) == 1:
+            i = params
+            if isinstance(i, int) and i >= 2:
+                return f"Qint{i}"
+
+    # def __new__(cls, name, bases, dct):
+    #     return _GetQintType()
+
+
+class Qint(metaclass=QintMeta):
+    pass
diff --git a/test/algo/test_grover.py b/test/algo/test_grover.py
index fd72db9e..001c97c6 100644
--- a/test/algo/test_grover.py
+++ b/test/algo/test_grover.py
@@ -23,7 +23,7 @@
 class TestAlgoGrover(unittest.TestCase):
     def test_grover(self):
         f = """
-def hash(k: Qint4) -> bool:
+def hash(k: Qint[4]) -> bool:
     h = True
     for i in range(4):
         h = h and k[i]
@@ -42,7 +42,7 @@ def hash(k: Qint4) -> bool:
 
     def test_grover_list_search(self):
         f = """
-def hash(k: Qint4) -> bool:
+def hash(k: Qint[4]) -> bool:
     h = False
     for i in [7]:
         if i == k:
@@ -62,7 +62,7 @@ def hash(k: Qint4) -> bool:
 
     def test_grover_without_element_to_search(self):
         f = """
-def hash(k: Qint4) -> bool:
+def hash(k: Qint[4]) -> bool:
     h = True
     for i in range(4):
         h = h and k[i]
@@ -80,7 +80,7 @@ def hash(k: Qint4) -> bool:
 
     def test_grover_too_many_args(self):
         f = """
-def hash(k: Qint4, q: Qint4) -> bool:
+def hash(k: Qint[4], q: Qint[4]) -> bool:
     h = True
     for i in range(4):
         h = h and (k[i] or q[i])
@@ -92,7 +92,7 @@ def hash(k: Qint4, q: Qint4) -> bool:
 
     def test_grover_subset_sum(self):
         f = """
-def subset_sum(ii: Tuple[Qint2, Qint2]) -> Qint2:
+def subset_sum(ii: Tuple[Qint[2], Qint[2]]) -> Qint[2]:
     l = [0, 1, 2, 0]
     return l[ii[0]] + l[ii[1]]
 """
diff --git a/test/algo/test_simon.py b/test/algo/test_simon.py
index 290a58f5..bca05f3c 100644
--- a/test/algo/test_simon.py
+++ b/test/algo/test_simon.py
@@ -26,7 +26,7 @@
 class TestAlgoSimon(unittest.TestCase):
     def test_simon(self):
         f = """
-def hash(k: Qint4) -> Qint4:
+def hash(k: Qint[4]) -> Qint[4]:
     return k >> 3
 """
         qf = qlassf(f, compiler=self.compiler)
diff --git a/test/ast2logic/test_translate_arg.py b/test/ast2logic/test_translate_arg.py
index 6937049a..01e640c0 100644
--- a/test/ast2logic/test_translate_arg.py
+++ b/test/ast2logic/test_translate_arg.py
@@ -16,9 +16,11 @@
 import unittest
 from typing import Tuple
 
+from parameterized import parameterized
+
 from qlasskit import Qint2, Qint4, ast2logic, exceptions
 from qlasskit.ast2ast import ast2ast
-from qlasskit.types.qfixed import Qfixed1_3
+from qlasskit.types import Qfixed1_3
 
 
 class TestAst2Logic_translate_argument(unittest.TestCase):
@@ -39,16 +41,16 @@ def test_bool(self):
         self.assertEqual(c.ttype, bool)
         self.assertEqual(c.bitvec, ["a"])
 
-    def test_qint2(self):
-        f = "a: Qint2"
+    @parameterized.expand(["a: Qint[2]", "a : Qint2"])
+    def test_qint2(self, f):
         ann_ast = ast2ast(ast.parse(f).body[0].annotation)
         c = ast2logic.translate_argument(ann_ast, ast2logic.Env(), "a")
         self.assertEqual(c.name, "a")
         self.assertEqual(c.ttype, Qint2)
         self.assertEqual(c.bitvec, ["a.0", "a.1"])
 
-    def test_qint4(self):
-        f = "a: Qint4"
+    @parameterized.expand(["a: Qint[4]", "a : Qint4"])
+    def test_qint4(self, f):
         ann_ast = ast2ast(ast.parse(f).body[0].annotation)
         c = ast2logic.translate_argument(ann_ast, ast2logic.Env(), "a")
         self.assertEqual(c.name, "a")
@@ -80,7 +82,7 @@ def test_tuple_of_tuple2(self):
         self.assertEqual(c.bitvec, ["a.0", "a.1.0", "a.1.1"])
 
     def test_tuple_of_int2(self):
-        f = "a: Tuple[Qint2, Qint2]"
+        f = "a: Tuple[Qint[2], Qint[2]]"
         ann_ast = ast2ast(ast.parse(f).body[0].annotation)
         c = ast2logic.translate_argument(ann_ast, ast2logic.Env(), "a")
         self.assertEqual(c.name, "a")
@@ -96,7 +98,7 @@ def test_tuple_of_int2(self):
         )
 
     def test_list_of_int2(self):
-        f = "a: Qlist[Qint2, 2]"
+        f = "a: Qlist[Qint[2], 2]"
         ann_ast = ast2ast(ast.parse(f)).body[0].annotation
         c = ast2logic.translate_argument(ann_ast, ast2logic.Env(), "a")
         self.assertEqual(c.name, "a")
diff --git a/test/qlassf/test_builtin.py b/test/qlassf/test_builtin.py
index 01fbcae9..e905904f 100644
--- a/test/qlassf/test_builtin.py
+++ b/test/qlassf/test_builtin.py
@@ -30,21 +30,21 @@ def test_print_call(self):
         compute_and_compare_results(self, qf)
 
     def test_len(self):
-        f = "def test(a: Tuple[bool, bool]) -> Qint2:\n\treturn len(a)"
+        f = "def test(a: Tuple[bool, bool]) -> Qint[2]:\n\treturn len(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[0][1], False)
         self.assertEqual(qf.expressions[1][1], True)
         compute_and_compare_results(self, qf)
 
     def test_len2(self):
-        f = "def test(a: Tuple[bool, bool]) -> Qint2:\n\tc=a\n\treturn len(c)"
+        f = "def test(a: Tuple[bool, bool]) -> Qint[2]:\n\tc=a\n\treturn len(c)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-2][1], False)
         self.assertEqual(qf.expressions[-1][1], True)
         compute_and_compare_results(self, qf)
 
     def test_len4(self):
-        f = "def test(a: Tuple[bool, bool, bool, bool]) -> Qint4:\n\treturn len(a)"
+        f = "def test(a: Tuple[bool, bool, bool, bool]) -> Qint[4]:\n\treturn len(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[0][1], False)
         self.assertEqual(qf.expressions[1][1], False)
@@ -53,58 +53,58 @@ def test_len4(self):
         compute_and_compare_results(self, qf)
 
     def test_min(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint2:\n\treturn min(a,b)"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[2]:\n\treturn min(a,b)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_min_const(self):
-        f = "def test(a: Qint2) -> Qint2:\n\treturn min(a,3)"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn min(a,3)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_max(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint2:\n\treturn max(a,b)"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[2]:\n\treturn max(a,b)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_max_of3(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint2:\n\treturn max(a,b,3)"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[2]:\n\treturn max(a,b,3)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_max_const(self):
-        f = "def test(a: Qint2) -> Qint2:\n\treturn max(a,3)"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn max(a,3)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     # TODO: fix cast
     # def test_max_const2(self):
-    #     f = "def test(a: Qint4) -> Qint4:\n\treturn max(a,3)"
+    #     f = "def test(a: Qint[4]) -> Qint[4]:\n\treturn max(a,3)"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
     def test_max_tuple(self):
-        f = "def test(a: Tuple[Qint2, Qint2]) -> Qint2:\n\treturn max(a)"
+        f = "def test(a: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\treturn max(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_max_tuple_const(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint2:\n\treturn max((a, b))"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[2]:\n\treturn max((a, b))"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_sum_const(self):
-        f = "def test() -> Qint2:\n\treturn sum([1,2,3])"
+        f = "def test() -> Qint[2]:\n\treturn sum([1,2,3])"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_sum_list(self):
-        f = "def test(a: Qlist[Qint2, 2]) -> Qint2:\n\treturn sum(a)"
+        f = "def test(a: Qlist[Qint[2], 2]) -> Qint[2]:\n\treturn sum(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_sum_tuple(self):
-        f = "def test(a: Tuple[Qint2, Qint2]) -> Qint2:\n\treturn sum(a)"
+        f = "def test(a: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\treturn sum(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -129,18 +129,18 @@ def test_all_const_list(self):
         compute_and_compare_results(self, qf)
 
     def test_max_in_list(self):
-        f = "def test() -> Qlist[Qint2, 3]:\n\treturn [max(0,1), max(1,2), max(2,3)]"
+        f = "def test() -> Qlist[Qint[2], 3]:\n\treturn [max(0,1), max(1,2), max(2,3)]"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     # TODO:
     # def test_len_of_range(self):
-    #     f = "def test() -> Qint4:\n\treturn len(range(4))"
+    #     f = "def test() -> Qint[4]:\n\treturn len(range(4))"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
     # TODO:
     # def test_range_of_len(self):
-    #     f = "def test(a: Qlist[bool, 3]) -> Qint4:\n\treturn range(len(a))"
+    #     f = "def test(a: Qlist[bool, 3]) -> Qint[4]:\n\treturn range(len(a))"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
diff --git a/test/qlassf/test_fixed.py b/test/qlassf/test_fixed.py
index f34a753f..0ea9912f 100644
--- a/test/qlassf/test_fixed.py
+++ b/test/qlassf/test_fixed.py
@@ -16,9 +16,8 @@
 
 from parameterized import parameterized, parameterized_class
 
-from qlasskit import Qint2, qlassf
-from qlasskit.types.qfixed import Qfixed1_3, Qfixed2_3, Qfixed2_4
-from qlasskit.types.qtype import bin_to_bool_list
+from qlasskit import qlassf
+from qlasskit.types import Qfixed1_3, Qfixed2_3, Qfixed2_4, Qint2, bin_to_bool_list
 
 from ..utils import COMPILATION_ENABLED, ENABLED_COMPILERS, compute_and_compare_results
 
@@ -162,11 +161,11 @@ def test_sub_const(self):
         compute_and_compare_results(self, qf)
 
     # def test_to_int(self):
-    #     f = "def test(a: Qfixed[2,4]) -> Qint2:\n\treturn int(a)"
+    #     f = "def test(a: Qfixed[2,4]) -> Qint[2]:\n\treturn int(a)"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
     # def test_to_float(self):
-    #     f = "def test(a: Qint2) -> Qfixed[2,4]:\n\treturn float(a)"
+    #     f = "def test(a: Qint[2]) -> Qfixed[2,4]:\n\treturn float(a)"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
diff --git a/test/qlassf/test_for_loop.py b/test/qlassf/test_for_loop.py
index 2ff0eed8..9d476c7d 100644
--- a/test/qlassf/test_for_loop.py
+++ b/test/qlassf/test_for_loop.py
@@ -28,17 +28,17 @@
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestForLoop(unittest.TestCase):
     def test_for_1it(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tfor x in range(1):\n\t\ta += 1\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tfor x in range(1):\n\t\ta += 1\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_for_4it(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tfor x in range(4):\n\t\ta += 1\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tfor x in range(4):\n\t\ta += 1\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_for_3it(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tfor i in range(3):\n\t\ta += i\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tfor i in range(3):\n\t\ta += i\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -61,7 +61,7 @@ def test_for_nit_bool_many(self):
 
     def test_for_cond(self):
         f = (
-            "def test(a: Qint2, b: bool) -> Qint2:\n\tfor i in range(2):\n"
+            "def test(a: Qint[2], b: bool) -> Qint[2]:\n\tfor i in range(2):\n"
             "\t\ta += (i if b else 1)\n\treturn a"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -69,7 +69,7 @@ def test_for_cond(self):
 
     def test_for_sum(self):
         f = (
-            "def hash(k: Qint4) -> bool:\n\tz = 1\n\tfor i in range(3):\n"
+            "def hash(k: Qint[4]) -> bool:\n\tz = 1\n\tfor i in range(3):\n"
             "\t\tz += i\n\treturn z == 3"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
diff --git a/test/qlassf/test_hybrid_quantum.py b/test/qlassf/test_hybrid_quantum.py
index fb0df652..9923ab96 100644
--- a/test/qlassf/test_hybrid_quantum.py
+++ b/test/qlassf/test_hybrid_quantum.py
@@ -29,14 +29,14 @@ def test_h(self):
     # THIS IS NOT ALLOWED, since hybrid quantum is applicable only with gates that
     # operates on 1 qubit a possible solution is to implement QFT directly as hybrid function.
     # def test_qft(self):
-    #     f = "def test(a: Qint2) -> Qint2:\n\treturn Q.QFT(a)"
+    #     f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn Q.QFT(a)"
     #     qf = qlassf(f, to_compile=False, uncompute=False)
     #     print(qf.expressions)
     #     count = qiskit_measure_and_count(qf.circuit().export(), 128)
     #     [self.assertEqual(x in count, True) for x in ["00", "11", "01", "11"]]
 
     def test_h_multi(self):
-        f = "def test(a: Qint2) -> Qint2:\n\treturn Q.H(a)"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn Q.H(a)"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, uncompute=False)
         count = qiskit_measure_and_count(qf.circuit().export(), 128)
         [self.assertEqual(x in count, True) for x in ["00", "11", "01", "11"]]
@@ -49,7 +49,7 @@ def test_bell(self):
         self.assertEqual(len(count.keys()), 2)
 
     def test_h_and_add(self):
-        f = "def test(a: Qint2) -> Qint2:\n\ta = Q.H(a)\n\treturn a + 1"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\ta = Q.H(a)\n\treturn a + 1"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, uncompute=False)
         count = qiskit_measure_and_count(qf.circuit().export(), 128)
         [self.assertEqual(x in count, True) for x in ["1110", "0011", "1001", "0100"]]
diff --git a/test/qlassf/test_ifthenelse.py b/test/qlassf/test_ifthenelse.py
index b7a714a5..a72beb40 100644
--- a/test/qlassf/test_ifthenelse.py
+++ b/test/qlassf/test_ifthenelse.py
@@ -75,7 +75,7 @@ def test_if_for(self):
 
     def test_if_for2(self):
         f = (
-            "def test(a: bool, b: bool) -> Qint2:\n"
+            "def test(a: bool, b: bool) -> Qint[2]:\n"
             + "\td = 0\n"
             + "\tfor i in range(3):\n"
             + "\t\tif a:\n"
diff --git a/test/qlassf/test_int.py b/test/qlassf/test_int.py
index bdeaa5c0..1af6ff15 100644
--- a/test/qlassf/test_int.py
+++ b/test/qlassf/test_int.py
@@ -14,11 +14,12 @@
 
 import unittest
 
-from parameterized import parameterized_class
+from parameterized import parameterized, parameterized_class
 from sympy import Symbol, symbols
 from sympy.logic import And, Not
 
 from qlasskit import Qint2, Qint4, exceptions, qlassf
+from qlasskit.types import bin_to_bool_list
 
 from ..utils import (
     COMPILATION_ENABLED,
@@ -32,21 +33,19 @@
 
 
 class TestQintEncoding(unittest.TestCase):
-    def test_fixed_const(self):
-        self.assertEqual(Qint4.to_bin(Qint4(6)), "0110")
-        self.assertEqual(Qint4.to_bin(Qint4(1)), "1000")
-        self.assertEqual(Qint4.to_bin(Qint4(2)), "0100")
-        self.assertEqual(Qint4.to_bin(Qint4(0)), "0000")
-        self.assertEqual(Qint4.to_bin(Qint4(8)), "0001")
-
-    def test_fixed_from_bool(self):
-        def fb(b):
-            return list(map(lambda c: True if c == "1" else False, b))
-
-        self.assertEqual(Qint4.from_bool(fb("0110")), 6)
-        self.assertEqual(Qint4.from_bool(fb("0010")), 4)
-        self.assertEqual(Qint4.from_bool(fb("0100")), 2)
-        self.assertEqual(Qint4.from_bool(fb("0001")), 8)
+    @parameterized.expand(
+        [
+            (Qint4, 6, "0110"),
+            (Qint4, 1, "1000"),
+            (Qint4, 2, "0100"),
+            (Qint4, 0, "0000"),
+            (Qint4, 8, "0001"),
+            (Qint4, 4, "0010"),
+        ]
+    )
+    def test_fixed_const(self, qit, val, bin_v):
+        self.assertEqual(qit.to_bin(qit(val)), bin_v)
+        self.assertEqual(qit.from_bool(bin_to_bool_list(bin_v)), val)
 
 
 class TestQint(unittest.TestCase):
@@ -62,12 +61,12 @@ def test_qint2_to_bin(self):
 
 
 @parameterized_class(
-    ("ttype", "ttype_str", "ttype_size", "compiler"),
+    ("ttype_str", "ttype_size", "compiler"),
     inject_parameterized_compilers(
         [
-            (Qint2, "Qint2", 2),
-            (Qint4, "Qint4", 4),
-            # (Qint8, "Qint8", 8),
+            ("Qint[2]", 2),
+            ("Qint[4]", 4),
+            # ("Qint[8]", 8),
         ]
     ),
 )
@@ -138,11 +137,11 @@ def test_int_const_compare_eq(self):
 
 
 @parameterized_class(
-    ("ttype", "ttype_str", "ttype_size", "compiler"),
+    ("ttype_str", "ttype_size", "compiler"),
     inject_parameterized_compilers(
         [
-            (Qint2, "Qint2", 2),
-            (Qint4, "Qint4", 4),
+            ("Qint[2]", 2),
+            ("Qint[4]", 4),
         ]
     ),
 )
@@ -178,7 +177,7 @@ def test_const_int_compare_lt(self):
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestQlassfInt(unittest.TestCase):
     def test_int_const(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc=2\n\treturn c"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tc=2\n\treturn c"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 2)
         self.assertEqual(qf.expressions[-2][1], False)
@@ -186,7 +185,7 @@ def test_int_const(self):
         compute_and_compare_results(self, qf)
 
     def test_int_const_compare_eq(self):
-        f = "def test(a: Qint2) -> bool:\n\treturn a == 2"
+        f = "def test(a: Qint[2]) -> bool:\n\treturn a == 2"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 1)
         self.assertEqual(qf.expressions[0][0], _ret)
@@ -194,7 +193,7 @@ def test_int_const_compare_eq(self):
         compute_and_compare_results(self, qf)
 
     def test_int_const_compare_eq_different_type(self):
-        f = "def test(a: Qint4) -> bool:\n\treturn a == 2"
+        f = "def test(a: Qint[4]) -> bool:\n\treturn a == 2"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 1)
         self.assertEqual(qf.expressions[0][0], _ret)
@@ -210,7 +209,7 @@ def test_int_const_compare_eq_different_type(self):
         compute_and_compare_results(self, qf)
 
     def test_const_int_compare_eq_different_type(self):
-        f = "def test(a: Qint4) -> bool:\n\treturn 2 == a"
+        f = "def test(a: Qint[4]) -> bool:\n\treturn 2 == a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 1)
         self.assertEqual(qf.expressions[0][0], _ret)
@@ -226,87 +225,87 @@ def test_const_int_compare_eq_different_type(self):
         compute_and_compare_results(self, qf)
 
     def test_const_int_compare_neq_different_type(self):
-        f = "def test(a: Qint4) -> bool:\n\treturn 2 != a"
+        f = "def test(a: Qint[4]) -> bool:\n\treturn 2 != a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_int_int_compare_neq(self):
-        f = "def test(a: Qint2, b: Qint2) -> bool:\n\treturn a != b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> bool:\n\treturn a != b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_const_int_compare_gt(self):
-        f = "def test(a: Qint4) -> bool:\n\treturn a > 6"
+        f = "def test(a: Qint[4]) -> bool:\n\treturn a > 6"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 1)
         self.assertEqual(qf.expressions[0][0], _ret)
         compute_and_compare_results(self, qf)
 
     # def test_int4_int4_compare_gt(self):
-    #     f = "def test(a: Qint4, b: Qint4) -> bool:\n\treturn a > b"
+    #     f = "def test(a: Qint[4], b: Qint[4]) -> bool:\n\treturn a > b"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     # self.assertEqual(len(qf.expressions), 1)
     #     # self.assertEqual(len(qf.expressions[0][0], _ret)
     #     compute_and_compare_results(self, qf)
 
     def test_const_int4_compare_lt(self):
-        f = "def test(a: Qint4) -> bool:\n\treturn a < 6"
+        f = "def test(a: Qint[4]) -> bool:\n\treturn a < 6"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(len(qf.expressions), 2)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_int_int_compare_gt(self):
-        f = "def test(a: Qint2, b: Qint2) -> bool:\n\treturn a > b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> bool:\n\treturn a > b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_int_int_compare_lt(self):
-        f = "def test(a: Qint2, b: Qint2) -> bool:\n\treturn a < b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> bool:\n\treturn a < b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_const_int_compare_gte(self):
-        f = "def test(a: Qint2, b: Qint2) -> bool:\n\treturn a >= b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> bool:\n\treturn a >= b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_const_int_compare_lte(self):
-        f = "def test(a: Qint2, b: Qint2) -> bool:\n\treturn a <= b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> bool:\n\treturn a <= b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-1][0], _ret)
         compute_and_compare_results(self, qf)
 
     def test_ite_return_qint(self):
-        f = "def test(a: bool, b: Qint2, c: Qint2) -> Qint2:\n\treturn b if a else c"
+        f = "def test(a: bool, b: Qint[2], c: Qint[2]) -> Qint[2]:\n\treturn b if a else c"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         self.assertEqual(qf.expressions[-2][0], Symbol("_ret.0"))
         self.assertEqual(qf.expressions[-1][0], Symbol("_ret.1"))
         compute_and_compare_results(self, qf)
 
     def test_composed_comparators(self):
-        f = "def f_comp(n: Qint4) -> bool: return n > 3 or n == 7"
+        f = "def f_comp(n: Qint[4]) -> bool: return n > 3 or n == 7"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_shift_left(self):
-        f = "def test(n: Qint2) -> Qint4: return n << 1"
+        f = "def test(n: Qint[2]) -> Qint[4]: return n << 1"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_shift_right(self):
-        f = "def test(n: Qint2) -> Qint4: return n >> 1"
+        f = "def test(n: Qint[2]) -> Qint[4]: return n >> 1"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     # Our Qint are unsigned
     # def test_invert_bitwise_not(self):
-    #     f = "def test(n: Qint4) -> bool: return ~n"
+    #     f = "def test(n: Qint[4]) -> bool: return ~n"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
@@ -315,32 +314,35 @@ def test_shift_right(self):
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestQlassfIntAdd(unittest.TestCase):
     def test_add_tuple(self):
-        f = "def test(a: Tuple[Qint2, Qint2]) -> Qint2: return a[0] + a[1]"
+        f = "def test(a: Tuple[Qint[2], Qint[2]]) -> Qint[2]: return a[0] + a[1]"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_add(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint2: return a + b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[2]: return a + b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_add_const(self):
-        f = "def test(a: Qint2) -> Qint2: return a + 1"
+        f = "def test(a: Qint[2]) -> Qint[2]: return a + 1"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_add_const2(self):
-        f = "def test() -> Qint4: return Qint4(3) + 3"
+        f = "def test() -> Qint[4]: return Qint4(3) + 3"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_add_const3(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint4: return Qint4(3) + a if a == 3 else Qint4(1) + b"
+        f = (
+            "def test(a: Qint[2], b: Qint[2]) -> Qint[4]: "
+            "return Qint4(3) + a if a == 3 else Qint4(1) + b"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_add_const4(self):
-        f = "def test(a: Qint2) -> Qint2: return a + 2"
+        f = "def test(a: Qint[2]) -> Qint[2]: return a + 2"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -359,12 +361,12 @@ def test_add_const4(self):
 )
 class TestQlassfIntMod(unittest.TestCase):
     def test_mod_const(self):
-        f = f"def test(a: Qint4) -> Qint4: return a % {self.val}"
+        f = f"def test(a: Qint[4]) -> Qint[4]: return a % {self.val}"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_mod_const_in_var(self):
-        f = f"def test(a: Qint4) -> Qint4:\n\tb = {self.val}\n\treturn a % b"
+        f = f"def test(a: Qint[4]) -> Qint[4]:\n\tb = {self.val}\n\treturn a % b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -372,27 +374,27 @@ def test_mod_const_in_var(self):
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestQlassfIntSub(unittest.TestCase):
     def test_sub_const(self):
-        f = "def test(a: Qint2) -> Qint2: return a - 1"
+        f = "def test(a: Qint[2]) -> Qint[2]: return a - 1"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_sub_const2(self):
-        f = "def test(a: Qint2) -> Qint2: return a - 3"
+        f = "def test(a: Qint[2]) -> Qint[2]: return a - 3"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_sub_const3(self):
-        f = "def test(a: Qint4) -> Qint4: return a - 8"
+        f = "def test(a: Qint[4]) -> Qint[4]: return a - 8"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
 
 @parameterized_class(
-    ("ttype", "ttype_str", "ttype_size", "compiler"),
+    ("ttype_str", "ttype_size", "compiler"),
     inject_parameterized_compilers(
         [
-            (Qint2, "Qint2", 2),
-            (Qint4, "Qint4", 4),
+            ("Qint[2]", 2),
+            ("Qint[4]", 4),
         ]
     ),
 )
@@ -425,22 +427,22 @@ def test_bitwise_xor(self):
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestQlassfIntReassign(unittest.TestCase):
     def test_reassign_newvar(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tb = 0\n\tb = a + 1\n\treturn b"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tb = 0\n\tb = a + 1\n\treturn b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_reassign_arg(self):
-        f = "def test(a: Qint2) -> Qint2:\n\ta = a + 1\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\ta = a + 1\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_aug_reassign_newvar(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tb = a\n\tb += 1\n\treturn b"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tb = a\n\tb += 1\n\treturn b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_aug_reassign_arg(self):
-        f = "def test(a: Qint2) -> Qint2:\n\ta += 1\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\ta += 1\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -448,32 +450,32 @@ def test_aug_reassign_arg(self):
 @parameterized_class(("compiler"), ENABLED_COMPILERS)
 class TestQlassfIntMul(unittest.TestCase):
     def test_mul(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint4: return a * b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[4]: return a * b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_mul_and_sum(self):
-        f = "def test(a: Qint2, b: Qint2, c: Qint2) -> Qint2: return a * b + c"
+        f = "def test(a: Qint[2], b: Qint[2], c: Qint[2]) -> Qint[2]: return a * b + c"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_mult_multconst(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint4:\n\treturn (a * b) * 5"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[4]:\n\treturn (a * b) * 5"
         qf = qlassf(f, compiler=self.compiler, to_compile=COMPILATION_ENABLED)
         compute_and_compare_results(self, qf)
 
     def test_mul_const(self):
-        f = "def test(a: Qint2, b: Qint4) -> Qint4:\n\treturn (a * 3) + b"
+        f = "def test(a: Qint[2], b: Qint[4]) -> Qint[4]:\n\treturn (a * 3) + b"
         qf = qlassf(f, compiler=self.compiler, to_compile=COMPILATION_ENABLED)
         compute_and_compare_results(self, qf)
 
     def test_mul4(self):
-        f = "def test(a: Qint4, b: Qint4) -> Qint8: return a * b"
+        f = "def test(a: Qint[4], b: Qint[4]) -> Qint[8]: return a * b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_mul5(self):
-        f = "def test(a: Qint3, b: Qint3) -> bool: return 3*2==6"
+        f = "def test(a: Qint[3], b: Qint[3]) -> bool: return 3*2==6"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
         self.assertEqual(qf.expressions[0][1], True)
diff --git a/test/qlassf/test_list.py b/test/qlassf/test_list.py
index 7d7ec4cf..2a146adf 100644
--- a/test/qlassf/test_list.py
+++ b/test/qlassf/test_list.py
@@ -46,7 +46,7 @@ def test_list(self):
         compute_and_compare_results(self, qf)
 
     def test_list_item_swap(self):
-        f = "def swapf(a: Qlist[Qint2, 2]) -> Qlist[Qint2, 2]:\n\treturn [a[1], a[0]]"
+        f = "def swapf(a: Qlist[Qint[2], 2]) -> Qlist[Qint[2], 2]:\n\treturn [a[1], a[0]]"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -56,35 +56,41 @@ def test_list_item_swap_bool(self):
         compute_and_compare_results(self, qf)
 
     def test_list_item_sum(self):
-        f = "def swapf(a: Qlist[Qint2, 2]) -> Qint2:\n\treturn a[0] + a[1]"
+        f = "def swapf(a: Qlist[Qint[2], 2]) -> Qint[2]:\n\treturn a[0] + a[1]"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_list_iterator_var(self):
-        f = "def test(a: Qlist[Qint2, 2]) -> Qint2:\n\tc = 0\n\tfor x in a:\n\t\tc += x\n\treturn c"
+        f = (
+            "def test(a: Qlist[Qint[2], 2]) -> Qint[2]:\n\tc = 0\n"
+            "\tfor x in a:\n\t\tc += x\n\treturn c"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_list_iterator_list(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc = 0\n\tfor x in [1,2,3]:\n\t\tc += x + a\n\treturn c"
+        f = (
+            "def test(a: Qint[2]) -> Qint[2]:\n\tc = 0\n"
+            "\tfor x in [1,2,3]:\n\t\tc += x + a\n\treturn c"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_list_iterator_varlist(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc = [1,2,3]\n\tfor x in c:\n\t\ta += x\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tc = [1,2,3]\n\tfor x in c:\n\t\ta += x\n\treturn a"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_list_len(self):
         f = (
-            "def test(a: Qlist[Qint2, 2]) -> Qint2:\n\tc = 0\n\tfor x in range(len(a)):\n"
+            "def test(a: Qlist[Qint[2], 2]) -> Qint[2]:\n\tc = 0\n\tfor x in range(len(a)):\n"
             "\t\tc += a[x]\n\treturn c"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_list_access_with_var(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc = [1,2,3,2]\n\tb = c[a]\n\treturn b"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\tc = [1,2,3,2]\n\tb = c[a]\n\treturn b"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -94,7 +100,7 @@ def test_list_access_with_var_on_tuple(self):
             return
 
         f = (
-            "def test(ab: Tuple[Qint2, Qint2]) -> Qint2:\n\tc = [1,2,3,2]\n\tai,bi = ab\n"
+            "def test(ab: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\tc = [1,2,3,2]\n\tai,bi = ab\n"
             "\td = c[ai] + c[bi]\n\treturn d"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -106,7 +112,7 @@ def test_list_access_with_var_on_tuple2(self):
             return
 
         f = (
-            "def test(ab: Tuple[Qint2, Qint2]) -> Qint2:\n\tc = [1,2,3,2]\n"
+            "def test(ab: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\tc = [1,2,3,2]\n"
             "\td = c[ab[0]] + c[ab[1]]\n\treturn d"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
diff --git a/test/qlassf/test_matrix.py b/test/qlassf/test_matrix.py
index f72730cc..ec91361e 100644
--- a/test/qlassf/test_matrix.py
+++ b/test/qlassf/test_matrix.py
@@ -59,7 +59,7 @@ def test_matrix_access(self):
 
     def test_matrix_iterator_var(self):
         f = (
-            "def test(a: Qmatrix[Qint2, 2, 2]) -> Qint2:\n\tc = 0\n\tfor x in a:\n"
+            "def test(a: Qmatrix[Qint[2], 2, 2]) -> Qint[2]:\n\tc = 0\n\tfor x in a:\n"
             "\t\tfor y in x:\n\t\t\tc += y\n\treturn c"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -67,7 +67,7 @@ def test_matrix_iterator_var(self):
 
     def test_matrix_iterator_list(self):
         f = (
-            "def test(a: Qint2) -> Qint2:\n\tc = 0\n\tfor x in [[1,2],[3,4]]:\n"
+            "def test(a: Qint[2]) -> Qint[2]:\n\tc = 0\n\tfor x in [[1,2],[3,4]]:\n"
             "\t\tfor y in x:\n\t\t\tc += y + a\n\treturn c"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -75,7 +75,7 @@ def test_matrix_iterator_list(self):
 
     def test_matrix_iterator_varlist(self):
         f = (
-            "def test(a: Qint2) -> Qint2:\n\tc = [[1,2],[3,4]]\n\tfor x in c:\n"
+            "def test(a: Qint[2]) -> Qint[2]:\n\tc = [[1,2],[3,4]]\n\tfor x in c:\n"
             "\t\tfor y in x:\n\t\t\ta += y\n\treturn a"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -83,7 +83,7 @@ def test_matrix_iterator_varlist(self):
 
     def test_matrix_len(self):
         f = (
-            "def test(a: Qmatrix[Qint2, 2, 2]) -> Qint2:\n\tc = 0\n"
+            "def test(a: Qmatrix[Qint[2], 2, 2]) -> Qint[2]:\n\tc = 0\n"
             "\tfor x in range(len(a)):\n\t\tc += a[x][0]\n\treturn c"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
@@ -93,13 +93,14 @@ def test_matrix_len(self):
     # (value=Name(id='a', ctx=Load()), slice=Name(id='i', ctx=Load()), ctx=Load())
     # def test_matrix_access2(self):
     #     f = (
-    #         "def test(a: Qmatrix[Qint2, 2, 2]) -> Qint2:\n\ti = 1\n\tj = i + 1\n\treturn a[i][i]"
+    #         "def test(a: Qmatrix[Qint[2], 2, 2]) -> Qint[2]:\n\ti = 1\n"
+    #           "\tj = i + 1\n\treturn a[i][i]"
     #     )
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
     # def test_matrix_access_with_var(self):
-    #     f = "def test(a: Qint2) -> Qint2:\n\tc = [[1,2],[3,4]]\n\tb = c[a][a]\n\treturn b"
+    #     f = "def test(a: Qint[2]) -> Qint[2]:\n\tc = [[1,2],[3,4]]\n\tb = c[a][a]\n\treturn b"
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
 
@@ -108,7 +109,7 @@ def test_matrix_len(self):
     #     if self.compiler == "internal":
     #         return
 
-    #     f = ("def test(ab: Tuple[Qint2, Qint2]) -> Qint2:\n\tc = [1,2,3,2]\n\tai,bi = ab\n"
+    #     f = ("def test(ab: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\tc = [1,2,3,2]\n\tai,bi = ab\n"
     #       "\td = c[ai] + c[bi]\n\treturn d")
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
@@ -118,7 +119,7 @@ def test_matrix_len(self):
     #     if self.compiler == "internal":
     #         return
 
-    #     f = ("def test(ab: Tuple[Qint2, Qint2]) -> Qint2:\n\tc = [1,2,3,2]\n"
+    #     f = ("def test(ab: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\tc = [1,2,3,2]\n"
     #       "\td = c[ab[0]] + c[ab[1]]\n\treturn d")
     #     qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
     #     compute_and_compare_results(self, qf)
diff --git a/test/qlassf/test_parameters.py b/test/qlassf/test_parameters.py
index 4b017efb..ef7841a6 100644
--- a/test/qlassf/test_parameters.py
+++ b/test/qlassf/test_parameters.py
@@ -37,7 +37,7 @@ def test_bind_bool(self):
 
     def test_bind_qint2(self):
         uqf = qlassf(
-            "def test(c: Parameter[Qint2], a: bool) -> Qint2: return c+1 if a else c",
+            "def test(c: Parameter[Qint[2]], a: bool) -> Qint[2]: return c+1 if a else c",
             to_compile=COMPILATION_ENABLED,
         )
         qf = uqf.bind(c=1)
@@ -46,7 +46,7 @@ def test_bind_qint2(self):
     def test_bind_multiple_qint2(self):
         uqf = qlassf(
             (
-                "def test(c: Parameter[Qint2], d: Parameter[Qint2], a: bool) -> Qint2: "
+                "def test(c: Parameter[Qint[2]], d: Parameter[Qint[2]], a: bool) -> Qint[2]: "
                 "return c+d if a else c+1"
             ),
             to_compile=COMPILATION_ENABLED,
@@ -64,7 +64,10 @@ def test_bind_list(self):
 
     def test_bind_tuple(self):
         uqf = qlassf(
-            "def test(c: Parameter[Tuple[bool, Qint2]]) -> Qint2: return c[1] if c[0] else c[1]+1",
+            (
+                "def test(c: Parameter[Tuple[bool, Qint[2]]]) -> Qint[2]:"
+                "return c[1] if c[0] else c[1]+1"
+            ),
             to_compile=COMPILATION_ENABLED,
         )
         qf = uqf.bind(c=(True, 2))
diff --git a/test/qlassf/test_qlassf.py b/test/qlassf/test_qlassf.py
index 186c94f0..34faf4b8 100644
--- a/test/qlassf/test_qlassf.py
+++ b/test/qlassf/test_qlassf.py
@@ -71,7 +71,7 @@ def test_encode_decode_bool(self):
         self.assertEqual(qf.decode_output("0"), False)
 
     def test_encode_decode_qint(self):
-        f = "def test(a: Qint2) -> Qint2:\n\treturn a"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn a"
         qf = qlassf(f, to_compile=False)
         self.assertEqual(qf.encode_input(Qint2(2)), "01"[::-1])
         self.assertEqual(qf.decode_output("01"[::-1]), Qint2(2))
@@ -80,7 +80,7 @@ def test_encode_decode_qint(self):
         self.assertEqual(qf.decode_output("00"), Qint2(0))
 
     def test_encode_decode_tuple(self):
-        f = "def test(a: Tuple[Qint2, bool]) -> Tuple[Qint2, bool]:\n\treturn a"
+        f = "def test(a: Tuple[Qint[2], bool]) -> Tuple[Qint[2], bool]:\n\treturn a"
         qf = qlassf(f, to_compile=False)
         self.assertEqual(qf.encode_input((Qint2(2), False)), "010")
         self.assertEqual(qf.decode_output("010"), (Qint2(2), False))
@@ -89,7 +89,7 @@ def test_encode_decode_tuple(self):
         self.assertEqual(qf.decode_output("001"[::-1]), (Qint2(0), True))
 
     def test_encode_decode_tuple2(self):
-        f = "def test(a: Tuple[Qint2, Qint4]) -> Tuple[Qint2, Qint4]:\n\treturn a"
+        f = "def test(a: Tuple[Qint[2], Qint[4]]) -> Tuple[Qint[2], Qint[4]]:\n\treturn a"
         qf = qlassf(f, to_compile=False)
         self.assertEqual(qf.encode_input((Qint2(2), Qint4(3))), "011100"[::-1])
         self.assertEqual(qf.decode_output("011100"[::-1]), (Qint2(2), Qint4(3)))
@@ -151,7 +151,7 @@ def test_or_not_truth(self):
         )
 
     def test_big_truth(self):
-        f = "def test(a: Qint4) -> Qint4:\n\treturn a"
+        f = "def test(a: Qint[4]) -> Qint[4]:\n\treturn a"
         qf = qlassf(f, to_compile=False)
         tt = qf.truth_table()
         tth = qf.truth_table_header()
diff --git a/test/qlassf/test_tuple.py b/test/qlassf/test_tuple.py
index 2438e71c..31baa6f1 100644
--- a/test/qlassf/test_tuple.py
+++ b/test/qlassf/test_tuple.py
@@ -46,7 +46,10 @@ def test_tuple_arg(self):
         compute_and_compare_results(self, qf)
 
     def test_tuple_item_swap(self):
-        f = "def swapf(a: Tuple[Qint2, Qint2]) -> Tuple[Qint2, Qint2]:\n\treturn (a[1], a[0])"
+        f = (
+            "def swapf(a: Tuple[Qint[2], Qint[2]]) -> Tuple[Qint[2], Qint[2]]:\n"
+            "\treturn (a[1], a[0])"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -140,12 +143,12 @@ def test_multi_assign(self):
         compute_and_compare_results(self, qf)
 
     def test_multi_assign2(self):
-        f = "def test() -> Qint4:\n\tc, d = 1, 2\n\treturn c+d"
+        f = "def test() -> Qint[4]:\n\tc, d = 1, 2\n\treturn c+d"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_multi_assign3(self):
-        f = "def test() -> Qint4:\n\tc, d, e = 1, 2, 0xa\n\treturn c+d+e"
+        f = "def test() -> Qint[4]:\n\tc, d, e = 1, 2, 0xa\n\treturn c+d+e"
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
@@ -165,24 +168,33 @@ def test_tuple_compare(self):
         compute_and_compare_results(self, qf)
 
     def test_tuple_int_compare(self):
-        f = "def test(a: Tuple[Qint2, Qint2], b: Tuple[Qint2, Qint2]) -> bool:\n\treturn a == b"
+        f = (
+            "def test(a: Tuple[Qint[2], Qint[2]], b: Tuple[Qint[2], Qint[2]]) -> bool:\n"
+            "\treturn a == b"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_tuple_iterator_var(self):
         f = (
-            "def test(a: Tuple[Qint2, Qint2]) -> Qint2:\n\tc = 0\n\tfor x in a:\n"
+            "def test(a: Tuple[Qint[2], Qint[2]]) -> Qint[2]:\n\tc = 0\n\tfor x in a:\n"
             "\t\tc += x\n\treturn c"
         )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_tuple_iterator_tuple(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc = 0\n\tfor x in (1,2,3):\n\t\tc += x + a\n\treturn c"
+        f = (
+            "def test(a: Qint[2]) -> Qint[2]:\n\tc = 0\n"
+            "\tfor x in (1,2,3):\n\t\tc += x + a\n\treturn c"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
 
     def test_tuple_iterator_vartuple(self):
-        f = "def test(a: Qint2) -> Qint2:\n\tc = (1,2,3)\n\tfor x in c:\n\t\ta += x\n\treturn a"
+        f = (
+            "def test(a: Qint[2]) -> Qint[2]:\n\tc = (1,2,3)\n"
+            "\tfor x in c:\n\t\ta += x\n\treturn a"
+        )
         qf = qlassf(f, to_compile=COMPILATION_ENABLED, compiler=self.compiler)
         compute_and_compare_results(self, qf)
diff --git a/test/test_compiler_regression.py b/test/test_compiler_regression.py
index 8f761f25..7d0082ad 100644
--- a/test/test_compiler_regression.py
+++ b/test/test_compiler_regression.py
@@ -35,7 +35,7 @@ def test_2(self):
         self.assertEqual(qc.num_qubits, 3)
 
     def test_3(self):
-        f = """def hash_simp(m: Qlist[Qint4, 2]) -> Qint8:
+        f = """def hash_simp(m: Qlist[Qint[4], 2]) -> Qint8:
     hv = 0
     for i in m:
         hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xff
diff --git a/test/test_decopt.py b/test/test_decopt.py
index 56b74ee5..0d1dc292 100644
--- a/test/test_decopt.py
+++ b/test/test_decopt.py
@@ -112,14 +112,14 @@ def test_circuit_boolean_optimizer_random_x_cx(self):
         # print(g_total, g_simp)
 
     def test_circuit_boolean_optimizer_duplicate_qubit_bug(self):
-        s = "def qf(a: Qint4) -> Qint4:\n\treturn a * a"
+        s = "def qf(a: Qint[4]) -> Qint[4]:\n\treturn a * a"
         qf = qlassf(s)
         qc = qf.circuit()
         nc = circuit_boolean_optimizer(qc)
         self.assertEqual(qc.num_gates, nc.num_gates)
         self.assertEqual(qc.num_qubits, nc.num_qubits)
 
-        s = "def qf(a: Qint4) -> Qint4:\n\treturn a + 2 + 1 + 3"
+        s = "def qf(a: Qint[4]) -> Qint[4]:\n\treturn a + 2 + 1 + 3"
         qf = qlassf(s)
         qc = qf.circuit()
         nc = circuit_boolean_optimizer(qc)
diff --git a/test/test_qlassf_to_bqm.py b/test/test_qlassf_to_bqm.py
index 8e539564..0d73add4 100644
--- a/test/test_qlassf_to_bqm.py
+++ b/test/test_qlassf_to_bqm.py
@@ -46,7 +46,7 @@ def test_to_bqm_1(self):
             self.assertEqual(x.sample["a"], True)
 
     def test_to_bqm_2(self):
-        f = "def test(a: Qint2) -> bool:\n\treturn a != 2"
+        f = "def test(a: Qint[2]) -> bool:\n\treturn a != 2"
         qf = qlassf(f, to_compile=False)
         bqm = qf.to_bqm()
         ss = sample_bqm(bqm)
@@ -58,7 +58,7 @@ def test_to_bqm_2(self):
             self.assertEqual(x.sample["a"], 2)
 
     def test_to_bqm_3(self):
-        f = "def test(a: Qint2) -> Qint2:\n\treturn a + 1"
+        f = "def test(a: Qint[2]) -> Qint[2]:\n\treturn a + 1"
         qf = qlassf(f, to_compile=False)
         bqm = qf.to_bqm()
         ss = sample_bqm(bqm)
@@ -70,7 +70,7 @@ def test_to_bqm_3(self):
             self.assertEqual(x.sample["a"] + 1 % 16, 0)
 
     def test_to_bqm_4(self):
-        f = "def test(a: Qint2, b: Qint2) -> Qint4:\n\treturn Qint4(0) + a + b"
+        f = "def test(a: Qint[2], b: Qint[2]) -> Qint[4]:\n\treturn Qint4(0) + a + b"
         qf = qlassf(f, to_compile=False)
         qubo, offset = qf.to_bqm("qubo")
         ss = sample_qubo(qubo)
@@ -93,7 +93,7 @@ def test_to_bqm_4(self):
     def test_to_bqm_subset_sum_problem(self):
         lst = [0, 5, 2, 3]
         f = (
-            f"def subset_sum(ii: Tuple[Qint2, Qint2]) -> Qint3:\n\tl = {lst}\n\t"
+            f"def subset_sum(ii: Tuple[Qint[2], Qint[2]]) -> Qint[3]:\n\tl = {lst}\n\t"
             "return l[ii[0]] + l[ii[1]] - 7"
         )
         qf = qlassf(f, to_compile=False)
@@ -117,7 +117,7 @@ def test_to_bqm_subset_sum_problem(self):
             self.assertEqual(sum(map(lambda i: lst[i], x.sample["ii"])), 7)
 
     def test_to_bqm_addends(self):
-        f = "def test(a: Qint4, b: Qint4) -> Qint4:\n\treturn a + b - 12"
+        f = "def test(a: Qint[4], b: Qint[4]) -> Qint[4]:\n\treturn a + b - 12"
         qf = qlassf(f, to_compile=False)
         bqm = qf.to_bqm()
 
@@ -130,7 +130,7 @@ def test_to_bqm_addends(self):
             self.assertEqual(x.sample["a"] + x.sample["b"], 12)
 
     def test_to_bqm_factoring(self):
-        f = "def test(a: Qint3, b: Qint3) -> Qint4:\n\treturn Qint4(15) - (a * b)"
+        f = "def test(a: Qint[3], b: Qint[3]) -> Qint[4]:\n\treturn Qint4(15) - (a * b)"
         qf = qlassf(f, to_compile=False)
         bqm = qf.to_bqm()
 
diff --git a/test/test_types.py b/test/test_types.py
index 90ffc547..9a49bbea 100644
--- a/test/test_types.py
+++ b/test/test_types.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Davide Gessa
+# Copyright 2023-204 Davide Gessa
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/test/utils.py b/test/utils.py
index 1284fa33..970d0fb8 100644
--- a/test/utils.py
+++ b/test/utils.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Davide Gessa
+# Copyright 2023-2024 Davide Gessa
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -23,8 +23,9 @@
 from qiskit_aer import Aer, AerSimulator
 from sympy.logic.boolalg import gateinputcount
 
-from qlasskit import Qint, Qtype, const_to_qtype
+from qlasskit import Qtype, const_to_qtype
 from qlasskit.qcircuit import CNotSim, GateNotSimulableException
+from qlasskit.types.qint import QintImp
 
 COMPILATION_ENABLED = True
 
@@ -81,7 +82,7 @@ def test_not(a: bool) -> bool:
     return not a
 
 
-class Qint14(Qint):
+class Qint14(QintImp):
     BIT_SIZE = 14