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[Feature] Diagonal operations handling in HamiltonianEvolution #282

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[Feature] Diagonal operations handling in HamiltonianEvolution #282

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31de6eb
apply operations specific to diagonal ops
Sep 16, 2024
793dc16
diagonal added in quantum op
Sep 16, 2024
4c863a2
setting diagonal primitives
Sep 17, 2024
08fe8b4
adding diagonal setter
Sep 17, 2024
976621b
diagonal option for sequence
Sep 17, 2024
fe507a6
adding diagonal for children sequence
Sep 17, 2024
7f06532
use diagonal op on scale and seq - successful test
Sep 18, 2024
6354188
adding Add in tests
Sep 18, 2024
ed51fc8
rm super init
Sep 18, 2024
dd35b6b
adding parametric diagonal ops
Sep 18, 2024
daeaba0
sequence to_diagonal
Sep 19, 2024
a45c5ff
add diagonal option for test random pauli hamevo
Sep 19, 2024
c7e39ae
change diagonal evolve
Sep 19, 2024
5286f96
is_diag_batched function in utils
Sep 19, 2024
00137d0
displace to_diagonal to quantum_operation
Sep 19, 2024
5633ce0
specifying diagonal fix
Sep 20, 2024
54fc0c1
fix Hamevo diagonal case but test as non-diagonal op
Sep 20, 2024
637d8a7
fix seq tensor
Sep 20, 2024
47d3820
change diagonal handling in hamevo
Sep 20, 2024
2713754
fixing dagger for adjoint
Sep 20, 2024
4793cb9
make merge diagonal also
Sep 20, 2024
7457f8b
add more docstrings
Sep 20, 2024
6b3740f
time dependent diagonal handling
Sep 20, 2024
69d5b3c
add eigenvalues test diagonal and not
Sep 20, 2024
582e2b4
replace einsum for diagonal
Sep 20, 2024
27285d0
item for diag batched
Sep 20, 2024
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307 changes: 307 additions & 0 deletions Untitled.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3517b062-6445-4502-8c92-6a486a55166d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyQTorch logger successfully setup with log level 20\n"
]
}
],
"source": [
"import pyqtorch as pyq\n",
"from pyqtorch import product_state\n",
"from pyqtorch.matrices import parametric_unitary, add_batch_dim\n",
"from pyqtorch.primitives import Primitive\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "30b5210d-3c7c-4dad-9676-5faf8138c70f",
"metadata": {},
"outputs": [],
"source": [
"state_111 = product_state(\"11\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "263a1ed0-36d9-48ab-92e4-b8ebdff352bd",
"metadata": {},
"outputs": [],
"source": [
"# torch.diag_embed(czop_example.tensor().squeeze(-1))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3f5d8bf0-98f8-4b07-b98c-9f3229c2ec4d",
"metadata": {},
"outputs": [],
"source": [
"# torch.diag_embed(torch.concatenate([czop_example.tensor(), czop_example.tensor(), czop_example.tensor()], axis=-1))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "11d6a6b5-5b50-447e-9a1c-58e864ec8656",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[ 2.+0.j],\n",
" [ 0.+0.j],\n",
" [ 0.+0.j],\n",
" [ 0.+0.j]],\n",
"\n",
" [[ 0.+0.j],\n",
" [ 2.+0.j],\n",
" [ 0.+0.j],\n",
" [ 0.+0.j]],\n",
"\n",
" [[ 0.+0.j],\n",
" [ 0.+0.j],\n",
" [-1.+0.j],\n",
" [ 1.+0.j]],\n",
"\n",
" [[ 0.+0.j],\n",
" [ 0.+0.j],\n",
" [ 1.+0.j],\n",
" [-1.+0.j]]], dtype=torch.complex128)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#czop_seq = pyq.Sequence([pyq.X(0), pyq.CNOT(control=0, target=1), czop_example, ])\n",
"czop_seq = pyq.Add([pyq.CNOT(control=0, target=1), pyq.Z(0, diagonal=False), ])\n",
"czop_seq.tensor()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "643905c7-6ee0-48a6-8b59-7c6d241256c1",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "einsum(): subscript i has size 2 for operand 1 which does not broadcast with previously seen size 4",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mijb,ikb->ijb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpyq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCNOT\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontrol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpyq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mZ\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdiagonal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Library/Application Support/hatch/env/virtual/pyqtorch/Gy8xvlfd/pyqtorch/lib/python3.10/site-packages/torch/functional.py:385\u001b[0m, in \u001b[0;36meinsum\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m einsum(equation, \u001b[38;5;241m*\u001b[39m_operands)\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(operands) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_einsum\u001b[38;5;241m.\u001b[39menabled:\n\u001b[1;32m 383\u001b[0m \u001b[38;5;66;03m# the path for contracting 0 or 1 time(s) is already optimized\u001b[39;00m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;66;03m# or the user has disabled using opt_einsum\u001b[39;00m\n\u001b[0;32m--> 385\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mequation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperands\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 387\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opt_einsum\u001b[38;5;241m.\u001b[39mis_available():\n",
"\u001b[0;31mRuntimeError\u001b[0m: einsum(): subscript i has size 2 for operand 1 which does not broadcast with previously seen size 4"
]
}
],
"source": [
"torch.einsum(\"ijb,ikb->ijb\", pyq.CNOT(control=0, target=1).tensor(), pyq.Z(0, diagonal=True).tensor().unsqueeze(1))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8e03808f-eff3-4e62-aedd-a43920f06e3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[ 2.+0.j],\n",
" [ 1.+0.j],\n",
" [ 1.+0.j],\n",
" [ 1.+0.j]],\n",
"\n",
" [[ 1.+0.j],\n",
" [ 2.+0.j],\n",
" [ 1.+0.j],\n",
" [ 1.+0.j]],\n",
"\n",
" [[-1.+0.j],\n",
" [-1.+0.j],\n",
" [-1.+0.j],\n",
" [ 0.+0.j]],\n",
"\n",
" [[-1.+0.j],\n",
" [-1.+0.j],\n",
" [ 0.+0.j],\n",
" [-1.+0.j]]], dtype=torch.complex128)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"czop_seq = pyq.Add([pyq.CNOT(control=0, target=1), pyq.Z(0, diagonal=True), ])\n",
"czop_seq.tensor()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23bfb221-43bd-4332-af56-7775ba370620",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e91ea5f6-d12e-410b-89dc-d69336aa5539",
"metadata": {},
"outputs": [],
"source": [
"# czop_diag_example.operation, czop_diag_example.identity"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39537b31-5a18-421f-bc43-d41e635c9a46",
"metadata": {},
"outputs": [],
"source": [
"# parametric_unitary(torch.tensor([1.0]), czop_diag_example.operation, czop_diag_example.identity, 1, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3898b9f-9679-4458-8c5b-8b33f19071ba",
"metadata": {},
"outputs": [],
"source": [
"val_param = {\"theta\": torch.Tensor([1.0])}\n",
"projector_apply_res = czop_example(state_111, val_param)\n",
"projector_apply_res_diag = czop_diag_example(state_111, val_param)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3246ef15-e3a8-43fa-bdcb-f07f1cc1b242",
"metadata": {},
"outputs": [],
"source": [
"torch.allclose(-state_111, projector_apply_res, atol=1.0e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0770d956-d6fb-4ca5-9245-69c79ece084b",
"metadata": {},
"outputs": [],
"source": [
"torch.allclose(-state_111, projector_apply_res_diag, atol=1.0e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d364a4e-189c-4d3d-9eb0-840065251b00",
"metadata": {},
"outputs": [],
"source": [
"torch.diag(czop_example.tensor()[...,0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd074ba2-c8fc-4e00-b402-9a26a5f6e34e",
"metadata": {},
"outputs": [],
"source": [
"torch.diag(czop_diag_example.tensor()[...,0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ac5790d-4142-40cc-999c-e1a351bf354c",
"metadata": {},
"outputs": [],
"source": [
"from pyqtorch.matrices import ZDIAG, controlled"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7369df6-3c40-481f-acc0-b18802e79704",
"metadata": {},
"outputs": [],
"source": [
"controlled(ZDIAG.unsqueeze(-1), batch_size=1, n_control_qubits=2, diagonal=True).squeeze(-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d681058-2861-4e46-8afa-6ffaa507bc94",
"metadata": {},
"outputs": [],
"source": [
"ZDIAG.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c70de5a5-c2db-4c0a-8689-91a58fe38452",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b3c21b0-6d03-4de1-a29f-52943c9d99e8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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