Ilc ansatz (#132)

* Add ILC ansatz class
* updates to qmf, qcc, and ilc ansatz classes
* enable QMF and ILC classes to read-in and process data from OpenFermion *.hdf5 files and other small fixes.

Co-authored-by: ValentinS4t1qbit <41597680+ValentinS4t1qbit@users.noreply.github.com>

tangelo/algorithms/variational/tests/test_vqe_solver.py

@@ -19,8 +19,6 @@
 from tangelo.algorithms import BuiltInAnsatze, VQESolver
 from tangelo.molecule_library import mol_H2_sto3g, mol_H4_sto3g, mol_H4_cation_sto3g, mol_NaH_sto3g, mol_H4_sto3g_symm
 from tangelo.toolboxes.ansatz_generator.uccsd import UCCSD
-from tangelo.toolboxes.ansatz_generator.qmf import QMF
-from tangelo.toolboxes.ansatz_generator.qcc import QCC
 from tangelo.toolboxes.qubit_mappings.mapping_transform import fermion_to_qubit_mapping
 from tangelo.toolboxes.molecular_computation.rdms import matricize_2rdm
 from tangelo.toolboxes.optimizers.rotosolve import rotosolve
@@ -119,7 +117,7 @@ def test_simulate_qmf_h2(self):
         """
 
         vqe_options = {"molecule": mol_H2_sto3g, "ansatz": BuiltInAnsatze.QMF, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 
@@ -131,7 +129,20 @@ def test_simulate_qcc_h2(self):
         parameters, exact simulator.
         """
         vqe_options = {"molecule": mol_H2_sto3g, "ansatz": BuiltInAnsatze.QCC, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
+        vqe_solver = VQESolver(vqe_options)
+        vqe_solver.build()
+
+        energy = vqe_solver.simulate()
+        self.assertAlmostEqual(energy, -1.137270, delta=1e-4)
+
+    def test_simulate_ilc_h2(self):
+        """Run VQE on H2 molecule, with ILC ansatz, JW qubit mapping, initial
+        parameters, exact simulator.
+        """
+
+        vqe_options = {"molecule": mol_H2_sto3g, "ansatz": BuiltInAnsatze.ILC, "qubit_mapping": "jw",
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 
@@ -209,7 +220,7 @@ def test_simulate_qmf_h4(self):
         """
 
         vqe_options = {"molecule": mol_H4_sto3g, "ansatz": BuiltInAnsatze.QMF, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 
@@ -220,14 +231,28 @@ def test_simulate_qcc_h4(self):
         """Run VQE on H4 molecule, with QCC ansatz, JW qubit mapping, initial
         parameters, exact simulator.
         """
+
         vqe_options = {"molecule": mol_H4_sto3g, "ansatz": BuiltInAnsatze.QCC, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 
         energy = vqe_solver.simulate()
         self.assertAlmostEqual(energy, -1.963270, delta=1e-4)
 
+    def test_simulate_ilc_h4(self):
+        """Run VQE on H4 molecule, with ILC ansatz, JW qubit mapping, initial
+        parameters, exact simulator.
+        """
+
+        vqe_options = {"molecule": mol_H4_sto3g, "ansatz": BuiltInAnsatze.ILC, "qubit_mapping": "jw",
+                        "verbose": False}
+        vqe_solver = VQESolver(vqe_options)
+        vqe_solver.build()
+
+        energy = vqe_solver.simulate()
+        self.assertAlmostEqual(energy, -1.960877, delta=1e-4)
+
     def test_simulate_h4_open(self):
         """Run VQE on H4 molecule, with UCCSD ansatz, JW qubit mapping, initial parameters, exact simulator """
         vqe_options = {"molecule": mol_H4_cation_sto3g, "ansatz": BuiltInAnsatze.UCCSD, "qubit_mapping": "jw",
@@ -244,7 +269,7 @@ def test_simulate_qmf_h4_open(self):
         """
 
         vqe_options = {"molecule": mol_H4_cation_sto3g, "ansatz": BuiltInAnsatze.QMF, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 
@@ -257,7 +282,20 @@ def test_simulate_qcc_h4_open(self):
         """
 
         vqe_options = {"molecule": mol_H4_cation_sto3g, "ansatz": BuiltInAnsatze.QCC, "qubit_mapping": "jw",
-                        "verbose": True}
+                        "verbose": False}
+        vqe_solver = VQESolver(vqe_options)
+        vqe_solver.build()
+
+        energy = vqe_solver.simulate()
+        self.assertAlmostEqual(energy, -1.638020, delta=1e-4)
+
+    def test_simulate_ilc_h4_open(self):
+        """Run VQE on H4 + molecule, with ILC ansatz, JW qubit mapping, initial
+        parameters, exact simulator.
+        """
+
+        vqe_options = {"molecule": mol_H4_cation_sto3g, "ansatz": BuiltInAnsatze.ILC, "qubit_mapping": "jw",
+                        "verbose": False}
         vqe_solver = VQESolver(vqe_options)
         vqe_solver.build()
 

tangelo/algorithms/variational/vqe_solver.py

@@ -29,7 +29,8 @@
 from tangelo.toolboxes.operators import count_qubits, FermionOperator, qubitop_to_qubitham
 from tangelo.toolboxes.qubit_mappings.mapping_transform import fermion_to_qubit_mapping
 from tangelo.toolboxes.ansatz_generator.ansatz import Ansatz
-from tangelo.toolboxes.ansatz_generator import UCCSD, RUCC, HEA, UpCCGSD, QMF, QCC, VSQS, UCCGD, VariationalCircuitAnsatz
+from tangelo.toolboxes.ansatz_generator import UCCSD, RUCC, HEA, UpCCGSD, QMF, QCC, VSQS, UCCGD,  ILC,\
+                                               VariationalCircuitAnsatz
 from tangelo.toolboxes.ansatz_generator.penalty_terms import combined_penalty
 from tangelo.toolboxes.post_processing.bootstrapping import get_resampled_frequencies
 from tangelo.toolboxes.ansatz_generator.fermionic_operators import number_operator, spinz_operator, spin2_operator
@@ -47,6 +48,7 @@ class BuiltInAnsatze(Enum):
     QCC = 6
     VSQS = 7
     UCCGD = 8
+    ILC = 9
 
 
 class VQESolver:
@@ -112,11 +114,11 @@ def __init__(self, opt_dict):
         if not (bool(self.molecule) ^ bool(self.qubit_hamiltonian)):
             raise ValueError(f"A molecule OR qubit Hamiltonian object must be provided when instantiating {self.__class__.__name__}.")
 
-        # The QCC ansatz requires up_then_down=True when mapping="jw"
+        # The QCC & ILC ansatze require up_then_down=True when mapping="jw"
         if isinstance(self.ansatz, BuiltInAnsatze):
-            if self.ansatz == BuiltInAnsatze.QCC and self.qubit_mapping.lower() == "jw" and not self.up_then_down:
-                warnings.warn("The QCC ansatz requires spin-orbital ordering to be all spin-up "
-                              "first followed by all spin-down for the JW mapping.", RuntimeWarning)
+            if self.ansatz in (BuiltInAnsatze.QCC, BuiltInAnsatze.ILC) and self.qubit_mapping.lower() == "jw" and not self.up_then_down:
+                warnings.warn("Efficient generator screening for QCC-based ansatze requires spin-orbital ordering to be "
+                              "all spin-up first followed by all spin-down for the JW mapping.", RuntimeWarning)
                 self.up_then_down = True
 
         self.default_backend_options = default_backend_options
@@ -193,6 +195,8 @@ def build(self):
                     self.ansatz = VSQS(self.molecule, self.qubit_mapping, self.up_then_down, **self.ansatz_options)
                 elif self.ansatz == BuiltInAnsatze.UCCGD:
                     self.ansatz = UCCGD(self.molecule, self.qubit_mapping, self.up_then_down, **self.ansatz_options)
+                elif self.ansatz == BuiltInAnsatze.ILC:
+                    self.ansatz = ILC(self.molecule, self.qubit_mapping, self.up_then_down, **self.ansatz_options)
                 else:
                     raise ValueError(f"Unsupported ansatz. Built-in ansatze:\n\t{self.builtin_ansatze}")
             elif not isinstance(self.ansatz, Ansatz):

tangelo/toolboxes/ansatz_generator/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from .vsqs import VSQS
+from .ilc import ILC
 from .qcc import QCC
 from .qmf import QMF
 from .uccsd import UCCSD

tangelo/toolboxes/ansatz_generator/_qubit_cc.py

@@ -33,10 +33,10 @@
 from itertools import combinations
 
 from tangelo.toolboxes.operators.operators import QubitOperator
-from ._qubit_mf import get_op_expval
+from tangelo.toolboxes.ansatz_generator._qubit_mf import get_op_expval
 
 
-def construct_dis(pure_var_params, qubit_ham, qcc_deriv_thresh, verbose=False):
+def construct_dis(qubit_ham, pure_var_params, deqcc_dtau_thresh):
     """Construct the DIS of QCC generators, which proceeds as follows:
     1. Identify the flip indices of all Hamiltonian terms and group terms by flip indices.
     2. Construct a representative generator using flip indices from each candidate DIS group
@@ -47,43 +47,36 @@ def construct_dis(pure_var_params, qubit_ham, qcc_deriv_thresh, verbose=False):
        odd number of Y operators.
 
     Args:
-        pure_var_params (numpy array of float): A purified QMF variational parameter set.
         qubit_ham (QubitOperator): A qubit Hamiltonian.
-        qcc_deriv_thresh (float): Threshold value of |dEQCC/dtau| so that if |dEQCC/dtau| >=
-            qcc_deriv_thresh for a generator, add its candidate group to the DIS.
-        verbose (bool): Flag for QCC verbosity.
+        pure_var_params (numpy array of float): A purified QMF variational parameter set.
+        deqcc_dtau_thresh (float): Threshold for |dEQCC/dtau| so that a candidate group is added
+            to the DIS if |dEQCC/dtau| >= deqcc_dtau_thresh for a generator.
 
     Returns:
         list of list: the DIS of QCC generators.
     """
 
     # Use a qubit Hamiltonian and purified QMF parameter set to construct the DIS
-    dis, dis_groups = [], get_dis_groups(pure_var_params, qubit_ham, qcc_deriv_thresh)
+    dis, dis_groups = [], get_dis_groups(qubit_ham, pure_var_params, deqcc_dtau_thresh)
     if dis_groups:
-        if verbose:
-            print(f"The DIS contains {len(dis_groups)} unique generator group(s).\n")
-        for i, dis_group in enumerate(dis_groups):
+        for dis_group in dis_groups:
             dis_group_idxs = [int(idxs) for idxs in dis_group[0].split(" ")]
             dis_group_gens = get_gens_from_idxs(dis_group_idxs)
             dis.append(dis_group_gens)
-            if verbose:
-                print(f"DIS group {i} | group size = {len(dis_group_gens)} | "
-                      f"flip indices = {dis_group_idxs} | |dEQCC/dtau| = "
-                      f"{abs(dis_group[1])} a.u.\n")
     else:
         raise ValueError(f"The DIS is empty: there are no candidate DIS groups where "
-                         f"|dEQCC/dtau| >= {qcc_deriv_thresh} a.u. Terminate the QCC simulation.\n")
+                         f"|dEQCC/dtau| >= {deqcc_dtau_thresh} a.u. Terminate simulation.\n")
     return dis
 
 
-def get_dis_groups(pure_var_params, qubit_ham, qcc_deriv_thresh):
+def get_dis_groups(qubit_ham, pure_var_params, deqcc_dtau_thresh):
     """Construct unique DIS groups characterized by the flip indices and |dEQCC/dtau|.
 
     Args:
-        pure_var_params (numpy array of float): A purified QMF variational parameter set.
         qubit_ham (QubitOperator): A qubit Hamiltonian.
-        qcc_deriv_thresh (float): Threshold value of |dEQCC/dtau| so that if |dEQCC/dtau| >=
-            qcc_deriv_thresh for a generator, add its candidate group to the DIS.
+        pure_var_params (numpy array of float): A purified QMF variational parameter set.
+        deqcc_dtau_thresh (float): Threshold for |dEQCC/dtau| so that a candidate group is added
+            to the DIS if |dEQCC/dtau| >= deqcc_dtau_thresh for a generator.
 
     Returns:
         list of tuple: the DIS group flip indices (str) and signed value of dEQCC/dtau (float).
@@ -94,15 +87,15 @@ def get_dis_groups(pure_var_params, qubit_ham, qcc_deriv_thresh):
                  for qham_items in qubit_ham.terms.items())
     flip_idxs = list(filter(None, (get_idxs_deriv(q_gen[0], *q_gen[1]) for q_gen in qham_gen)))
 
-    # Group Hamiltonian terms with the same flip indices and sum signed dEQCC/tau values
+    # Group Hamiltonian terms with the same flip indices and sum of the signed dEQCC/tau values
     candidates = dict()
     for idxs in flip_idxs:
         deriv_old = candidates.get(idxs[0], 0.)
         candidates[idxs[0]] = idxs[1] + deriv_old
 
     # Return a sorted list of flip indices and signed dEQCC/dtau values for each DIS group
     dis_groups = [idxs_deriv for idxs_deriv in candidates.items()
-                  if abs(idxs_deriv[1]) >= qcc_deriv_thresh]
+                  if abs(idxs_deriv[1]) >= deqcc_dtau_thresh]
     return sorted(dis_groups, key=lambda deriv: abs(deriv[1]), reverse=True)
 
 
@@ -124,18 +117,18 @@ def get_idxs_deriv(qham_term, *qham_qmf_data):
     """
 
     coef, pure_params = qham_qmf_data
-    idxs, gen_list, idxs_deriv = "", [], None
+    idxs, gen_tup, idxs_deriv = "", tuple(), None
     for pauli_factor in qham_term:
         # The indices of X and Y operators are flip indices
         idx, pauli_op = pauli_factor
         if "X" in pauli_op or "Y" in pauli_op:
             gen = (idx, "Y") if idxs == "" else (idx, "X")
             idxs = idxs + f" {idx}" if idxs != "" else f"{idx}"
-            gen_list.append(gen)
+            gen_tup += (gen, )
     # Generators must have at least two flip indices
-    if len(gen_list) > 1:
+    if len(gen_tup) > 1:
         qham_gen_comm = QubitOperator(qham_term, -1j * coef)
-        qham_gen_comm *= QubitOperator(tuple(gen_list), 1.)
+        qham_gen_comm *= QubitOperator(gen_tup, 1.)
         deriv = get_op_expval(qham_gen_comm, pure_params).real
         idxs_deriv = (idxs, deriv)
     return idxs_deriv