Trotterizable mixin

src/qforte/abc/algorithm.py

@@ -7,6 +7,7 @@
 from abc import ABC, abstractmethod
 import qforte as qf
 from qforte.utils.state_prep import *
+from qforte.abc.mixin import Trotterizable
 
 
 class Algorithm(ABC):
@@ -30,16 +31,6 @@ class Algorithm(ABC):
         measurment (unphysical for quantum computer). Most algorithms only
         have a fast implentation.
 
-    _trotter_order : int
-        The Trotter order to use for exponentiated operators.
-        (exact in the infinite limit).
-
-    _trotter_number : int
-        The number of trotter steps (m) to perform when approximating the matrix
-        exponentials (Um or Un). For the exponential of two non commuting terms
-        e^(A + B), the approximate operator C(m) = (e^(A/m) * e^(B/m))^m is
-        exact in the infinite m limit.
-
     _Egs : float
         The final ground state energy value.
 
@@ -69,8 +60,6 @@ def __init__(
         system,
         reference=None,
         state_prep_type="occupation_list",
-        trotter_order=1,
-        trotter_number=1,
         fast=True,
         verbose=False,
         print_summary_file=False,
@@ -119,8 +108,7 @@ def __init__(
             self._hf_energy = 0.0
 
         self._Nl = len(self._qb_ham.terms())
-        self._trotter_order = trotter_order
-        self._trotter_number = trotter_number
+
         self._fast = fast
         self._verbose = verbose
         self._print_summary_file = print_summary_file
@@ -202,6 +190,20 @@ def verify_required_attributes(self):
                 "Concrete Algorithm class must define self._n_pauli_trm_measures attribute."
             )
 
+    def print_generic_options(self):
+        """Print options applicable to any algorithm."""
+        print(
+            "Trial reference state:                   ",
+            ref_string(self._ref, self._nqb),
+        )
+        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
+        print("Trial state preparation method:          ", self._state_prep_type)
+        if isinstance(self, Trotterizable):
+            self.print_trotter_options()
+        print("Use fast version of algorithm:           ", str(self._fast))
+        if not self._fast:
+            print("Measurement variance thresh:             ", 0.01)
+
 
 class AnsatzAlgorithm(Algorithm):
     """A class that characterizes the most basic functionality for all

src/qforte/abc/ansatz.py

@@ -11,13 +11,17 @@
 from qforte.utils.state_prep import build_Uprep
 from qforte.utils.trotterization import trotterize
 from qforte.utils.compact_excitation_circuits import compact_excitation_circuit
+from qforte.abc.mixin import Trotterizable
 
 
-class UCC:
+class UCC(Trotterizable):
     """A mixin class for implementing the UCC circuit ansatz, to be inherited by a
     concrete class UCC+algorithm class.
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
     def ansatz_circuit(self, amplitudes=None):
         """This function returns the Circuit object built
         from the appropriate amplitudes.

src/qforte/abc/mixin.py

@@ -0,0 +1,23 @@
+class Trotterizable:
+    """
+    A mixin class for methods that employ Trotter approximation.
+
+    _trotter_order : int
+        The Trotter order to use for exponentiated operators.
+        (exact in the infinite limit).
+
+    _trotter_number : int
+        The number of trotter steps (m) to perform when approximating the matrix
+        exponentials (Um or Un). For the exponential of two non commuting terms
+        e^(A + B), the approximate operator C(m) = (e^(A/m) * e^(B/m))^m is
+        exact in the infinite m limit.
+    """
+    def __init__(self, *args, trotter_order=1, trotter_number=1, **kwargs):
+       super().__init__(*args, **kwargs)
+       self._trotter_order = trotter_order
+       self._trotter_number = trotter_number 
+
+    def print_trotter_options(self):
+         print("Trotter order (rho):                     ", self._trotter_order)
+         print("Trotter number (m):                      ", self._trotter_number)
+

src/qforte/abc/uccpqeabc.py

@@ -18,7 +18,7 @@
 import numpy as np
 
 
-class UCCPQE(PQE, UCC):
+class UCCPQE(UCC, PQE):
     """The abstract base class inheritied by any algorithm that seeks to find
     eigenstates by minimization of the residual condition
 

src/qforte/abc/uccvqeabc.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 
-class UCCVQE(VQE, UCC):
+class UCCVQE(UCC, VQE):
     """The abstract base class inheritied by any algorithm that seeks to find
     eigenstates by variational minimization of the Energy
 
@@ -70,6 +70,9 @@ class UCCVQE(VQE, UCC):
 
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
     @abstractmethod
     def get_num_ham_measurements(self):
         pass

src/qforte/ite/qite.py

@@ -6,6 +6,7 @@
 """
 import qforte as qf
 from qforte.abc.algorithm import Algorithm
+from qforte.abc.mixin import Trotterizable
 from qforte.utils.transforms import get_jw_organizer, organizer_to_circuit
 
 from qforte.utils.state_prep import *
@@ -20,7 +21,7 @@
 ### Throughout this file, we'll refer to DOI 10.1038/s41567-019-0704-4 as Motta.
 
 
-class QITE(Algorithm):
+class QITE(Trotterizable, Algorithm):
     """This class implements the quantum imaginary time evolution (QITE)
     algorithm in a fashion amenable to non k-local hamiltonains, which is
     the focus of the origional algorithm (see DOI 10.1038/s41567-019-0704-4).
@@ -185,18 +186,8 @@ def print_options_banner(self):
 
         print("\n\n                 ==> QITE options <==")
         print("-----------------------------------------------------------")
-        # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if not self._fast:
-            print("Measurement variance thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         # Specific QITE options.
         print("Total imaginary evolution time (beta):   ", self._beta)

src/qforte/qkd/mrsqk.py

@@ -7,6 +7,7 @@
 """
 
 import qforte
+from qforte.abc.mixin import Trotterizable
 from qforte.abc.qsdabc import QSD
 from qforte.qkd.srqk import SRQK
 from qforte.helper.printing import matprint
@@ -27,7 +28,7 @@
 from scipy.linalg import eig
 
 
-class MRSQK(QSD):
+class MRSQK(Trotterizable, QSD):
     """A quantum subspace diagonalization algorithm that generates the many-body
     basis from :math:`s` real time evolutions of :math:`d` differnt orthogonal
     reference states :math:`| \Phi_I \\rangle`:
@@ -224,19 +225,8 @@ def print_options_banner(self):
 
         print("\n\n                 ==> MRSQK options <==")
         print("-----------------------------------------------------------")
-        # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement varience thresh:             ", "NA")
-        else:
-            print("Measurement varience thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         # Specific QITE options.
         print("Dimension of reference space (d):        ", self._d)

src/qforte/qkd/nt_srqk.py

@@ -80,13 +80,8 @@ def print_options_banner(self):
 
         print("\n\n                   ==> NTQK options <==")
         print("-----------------------------------------------------------")
-        # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
+
+        self.print_generic_options()
 
         # Specific SRQK options.
         print("Dimension of Krylov space (N):           ", self._nstates)

src/qforte/qkd/srqk.py

@@ -7,6 +7,7 @@
 """
 
 import qforte
+from qforte.abc.mixin import Trotterizable
 from qforte.abc.qsdabc import QSD
 from qforte.helper.printing import matprint
 
@@ -18,7 +19,7 @@
 import numpy as np
 
 
-class SRQK(QSD):
+class SRQK(Trotterizable, QSD):
     """A quantum subspace diagonalization algorithm that generates the many-body
     basis from different durations of real time evolution:
 
@@ -73,20 +74,8 @@ def print_options_banner(self):
 
         print("\n\n                     ==> QK options <==")
         print("-----------------------------------------------------------")
-        # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement varience thresh:             ", "NA")
-        else:
-            print("Measurement varience thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         # Specific SRQK options.
         print("Dimension of Krylov space (N):           ", self._nstates)

src/qforte/qpea/qpe.py

@@ -1,5 +1,6 @@
 import qforte
 from qforte.abc.algorithm import Algorithm
+from qforte.abc.mixin import Trotterizable
 from qforte.utils.transforms import (
     circuit_to_organizer,
     organizer_to_circuit,
@@ -15,7 +16,7 @@
 from scipy import stats
 
 
-class QPE(Algorithm):
+class QPE(Trotterizable, Algorithm):
     def run(
         self, guess_energy: float, t=1.0, nruns=20, success_prob=0.5, num_precise_bits=4
     ):
@@ -147,15 +148,7 @@ def print_options_banner(self):
         print("\n\n                 ==> QPE options <==")
         print("-----------------------------------------------------------")
         # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        print("Measurement variance thresh:             ", "NA" if self._fast else 0.01)
+        self.print_generic_options()
 
         # Specific QPE options.
         print("Target success probability:              ", self._success_prob)

src/qforte/ucc/adaptvqe.py

@@ -250,20 +250,8 @@ def print_options_banner(self):
 
         print("\n\n               ==> ADAPT-VQE options <==")
         print("---------------------------------------------------------")
-        # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement varience thresh:             ", "NA")
-        else:
-            print("Measurement varience thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         print("Use qubit excitations:                   ", self._qubit_excitations)
         print("Use compact excitation circuits:         ", self._compact_excitations)

src/qforte/ucc/spqe.py

@@ -325,19 +325,8 @@ def print_options_banner(self):
 
         print("\n\n               ==> SPQE options <==")
         print("---------------------------------------------------------")
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement varience thresh:             ", "NA")
-        else:
-            print("Measurement varience thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         print("Use qubit excitations:                   ", self._qubit_excitations)
         print("Use compact excitation circuits:         ", self._compact_excitations)

src/qforte/ucc/uccnpqe.py

@@ -145,19 +145,8 @@ def print_options_banner(self):
 
         print("\n\n                 ==> UCC-PQE options <==")
         print("---------------------------------------------------------")
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement varience thresh:             ", "NA")
-        else:
-            print("Measurement varience thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         print("Use qubit excitations:                   ", self._qubit_excitations)
         print("Use compact excitation circuits:         ", self._compact_excitations)

src/qforte/ucc/uccnvqe.py

@@ -130,19 +130,8 @@ def print_options_banner(self):
         print("\n\n               ==> UCCN-VQE options <==")
         print("---------------------------------------------------------")
         # General algorithm options.
-        print(
-            "Trial reference state:                   ",
-            ref_string(self._ref, self._nqb),
-        )
-        print("Number of Hamiltonian Pauli terms:       ", self._Nl)
-        print("Trial state preparation method:          ", self._state_prep_type)
-        print("Trotter order (rho):                     ", self._trotter_order)
-        print("Trotter number (m):                      ", self._trotter_number)
-        print("Use fast version of algorithm:           ", str(self._fast))
-        if self._fast:
-            print("Measurement variance thresh:             ", "NA")
-        else:
-            print("Measurement variance thresh:             ", 0.01)
+
+        self.print_generic_options()
 
         print("Use qubit excitations:                   ", self._qubit_excitations)
         print("Use compact excitation circuits:         ", self._compact_excitations)