Staged controlled time

.github/workflows/github_actions_automated_testing.yml

@@ -37,7 +37,7 @@ jobs:
 
     - name: Install backends except qsharp/qdk
       run: |
-        pip install qiskit
+        pip install qiskit==0.33.1
         pip install qulacs
         pip install amazon-braket-sdk
         pip install cirq==0.12.0

tangelo/algorithms/projective/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2021 Good Chemistry Company.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

tangelo/algorithms/projective/quantum_imaginary_time.py

@@ -0,0 +1,315 @@
+# Copyright 2021 Good Chemistry Company.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Module that defines the Quantum Imaginary Time Algorithm (QITE)
+"""
+from copy import copy
+import math
+
+from openfermion import FermionOperator as ofFermionOperator
+import numpy as np
+
+from tangelo.toolboxes.ansatz_generator.ansatz_utils import trotterize
+from tangelo.toolboxes.operators.operators import FermionOperator, QubitOperator
+from tangelo.toolboxes.qubit_mappings.mapping_transform import fermion_to_qubit_mapping
+from tangelo.toolboxes.ansatz_generator._general_unitary_cc import uccgsd_generator as uccgsd_pool
+from tangelo.toolboxes.operators import qubitop_to_qubitham
+from tangelo.toolboxes.qubit_mappings.statevector_mapping import get_reference_circuit
+from tangelo.linq import Circuit, Simulator
+
+
+class QITESolver:
+    """QITE class. This is an iterative algorithm that obtains a unitary operator
+    that approximates the imaginary time evolution of an initial state.
+
+    Attributes:
+        molecule (SecondQuantizedMolecule): The molecular system.
+        dt (float): The imaginary time step size
+        min_de (float): Maximum energy change allowed before convergence.
+        max_cycles (int): Maximum number of iterations of QITE.
+        pool (func): Function that returns a list of FermionOperator. Each
+            element represents an excitation/operator that has an effect on the
+            total energy.
+        pool_args (tuple) : The arguments for the pool function given as a
+            tuple.
+        qubit_mapping (str): One of the supported qubit mapping identifiers.
+        qubit_hamiltonian (QubitOperator-like): Self-explanatory.
+        up_then_down (bool): Spin orbitals ordering.
+        n_spinorbitals (int): Self-explanatory.
+        n_electrons (int): Self-explanatory.
+        backend_options (dict): Backend options for the underlying QITE propagation
+        verbose (bool): Flag for verbosity of QITE.
+     """
+
+    def __init__(self, opt_dict):
+
+        default_backend_options = {"target": None, "n_shots": None, "noise_model": None}
+        default_options = {"molecule": None,
+                           "dt": 0.5, "max_cycles": 100,
+                           "min_de": 1.e-7,
+                           "pool": uccgsd_pool,
+                           "pool_args": None,
+                           "frozen_orbitals": "frozen_core",
+                           "qubit_mapping": "jw",
+                           "qubit_hamiltonian": None,
+                           "up_then_down": False,
+                           "n_spinorbitals": None,
+                           "n_electrons": None,
+                           "backend_options": default_backend_options,
+                           "verbose": True}
+
+        # Initialize with default values
+        self.__dict__ = default_options
+        # Overwrite default values with user-provided ones, if they correspond to a valid keyword
+        for k, v in opt_dict.items():
+            if k in default_options:
+                setattr(self, k, v)
+            else:
+                raise KeyError(f"Keyword :: {k}, not available in {self.__class__.__name__}")
+
+        # Raise error/warnings if input is not as expected. Only a single input
+        # must be provided to avoid conflicts.
+        if not (bool(self.molecule) ^ bool(self.qubit_hamiltonian)):
+            raise ValueError("A molecule OR qubit Hamiltonian object must be provided when instantiating "
+                             f"{self.__class__.__name__}.")
+
+        if self.qubit_hamiltonian:
+            if not (self.n_spinorbitals and self.n_electrons):
+                raise ValueError("Expecting the number of spin-orbitals (n_spinorbitals) and the number of "
+                                 "electrons (n_electrons) with a qubit_hamiltonian.")
+
+        self.iteration = 0
+        self.energies = list()
+
+        self.circuit_list = list()
+        self.final_energy = None
+        self.final_circuit = None
+        self.final_statevector = None
+
+        self.backend = None
+
+    def prepare_reference_state(self):
+        """Returns circuit preparing the reference state of the ansatz (e.g
+        prepare reference wavefunction with HF, multi-reference state, etc).
+        These preparations must be consistent with the transform used to obtain
+        the qubit operator.
+        """
+
+        return get_reference_circuit(n_spinorbitals=self.n_spinorbitals,
+                                     n_electrons=self.n_electrons,
+                                     mapping=self.qubit_mapping,
+                                     up_then_down=self.up_then_down,
+                                     spin=0)
+
+    def build(self):
+        """Builds the underlying objects required to run the QITE algorithm."""
+
+        # Building molecule data with a pyscf molecule.
+        if self.molecule:
+
+            self.n_spinorbitals = self.molecule.n_active_sos
+            self.n_electrons = self.molecule.n_active_electrons
+
+            # Compute qubit hamiltonian for the input molecular system
+            qubit_op = fermion_to_qubit_mapping(fermion_operator=self.molecule.fermionic_hamiltonian,
+                                                mapping=self.qubit_mapping,
+                                                n_spinorbitals=self.n_spinorbitals,
+                                                n_electrons=self.n_electrons,
+                                                up_then_down=self.up_then_down,
+                                                spin=0)
+
+            self.qubit_hamiltonian = qubitop_to_qubitham(qubit_op, self.qubit_mapping, self.up_then_down)
+
+        # Getting the pool of operators for the ansatz. If more functionalities
+        # are added, this part must be modified and generalized.
+        if self.pool_args is None:
+            if self.pool == uccgsd_pool:
+                self.pool_args = (self.n_spinorbitals,)
+            else:
+                raise KeyError("pool_args must be defined if using own pool function")
+        # Check if pool function returns a QubitOperator or FermionOperator and populate variables
+        pool_list = self.pool(*self.pool_args)
+        if isinstance(pool_list[0], QubitOperator):
+            self.pool_type = "qubit"
+            self.full_pool_operators = pool_list
+        elif isinstance(pool_list[0], (FermionOperator, ofFermionOperator)):
+            self.pool_type = "fermion"
+            self.fermionic_operators = pool_list
+            self.full_pool_operators = [fermion_to_qubit_mapping(fermion_operator=fi,
+                                                                 mapping=self.qubit_mapping,
+                                                                 n_spinorbitals=self.n_spinorbitals,
+                                                                 n_electrons=self.n_electrons,
+                                                                 up_then_down=self.up_then_down) for fi in self.fermionic_operators]
+        else:
+            raise ValueError("pool function must return either QubitOperator or FermionOperator")
+
+        # Cast all coefs to floats (rotations angles are real).
+        for qubit_op in self.full_pool_operators:
+            for term, coeff in qubit_op.terms.items():
+                qubit_op.terms[term] = math.copysign(1., coeff.imag)
+
+        # Remove duplicates and only select terms with odd number of Y gates for all mappings except JKMN
+        if self.qubit_mapping.upper() != "JKMN":
+            reduced_pool_terms = set()
+            for qubit_op in self.full_pool_operators:
+                for term in qubit_op.terms:
+                    count_y = str(term).count("Y")
+                    if count_y % 2 == 1:
+                        reduced_pool_terms.add(term)
+        else:
+            reduced_pool_terms = set()
+            for qubit_op in self.full_pool_operators:
+                for term in qubit_op.terms.keys():
+                    if term:
+                        reduced_pool_terms.add(term)
+
+        # Generated list of pool_operators and full pool operator.
+        self.pool_operators = [QubitOperator(term) for term in reduced_pool_terms]
+        self.pool_qubit_op = QubitOperator()
+        for term in self.pool_operators:
+            self.pool_qubit_op += term
+
+        self.qubit_operator = self.qubit_hamiltonian.to_qubitoperator()
+
+        # Obtain all qubit terms that need to be measured
+        self.pool_h = [element*self.qubit_operator for element in self.pool_operators]
+        self.pool_pool = [[element1*element2 for element2 in self.pool_operators] for element1 in self.pool_operators]
+
+        # Obtain initial state preparation circuit
+        self.circuit_list.append(self.prepare_reference_state())
+        self.final_circuit = copy(self.circuit_list[0])
+
+        # Quantum circuit simulation backend options
+        self.backend = Simulator(target=self.backend_options["target"], n_shots=self.backend_options["n_shots"],
+                                 noise_model=self.backend_options["noise_model"])
+
+        self.use_statevector = self.backend.statevector_available and self.backend._noise_model is None
+
+    def simulate(self):
+        """Performs the QITE cycles. Each iteration, a linear system is
+        solved to obtain the next unitary. The system to be solved can be found in
+        section 3.5 of https://arxiv.org/pdf/2108.04413.pdf
+
+        Returns:
+            float: final energy after obtaining running QITE
+        """
+
+        # Construction of the circuit. self.max_cycles terms are added, unless
+        # the energy change is less than self.min_de.
+        if self.use_statevector:
+            self.update_statevector(self.backend, self.circuit_list[0])
+        self.final_energy = self.energy_expectation(self.backend)
+        self.energies.append(self.final_energy)
+        while self.iteration < self.max_cycles:
+            self.iteration += 1
+            if self.verbose:
+                print(f"Iteration {self.iteration} of QITE with starting energy {self.final_energy}")
+
+            suv, bu = self.calculate_matrices(self.backend, self.final_energy)
+
+            alphas_array = np.linalg.solve(suv.real, bu.real)
+            # convert to dictionary with key as first (only) term of each pool_operator and value self.dt * alphas_array[i]
+            alphas_dict = {next(iter(qu_op.terms)): self.dt * alphas_array[i] for i, qu_op in enumerate(self.pool_operators)}
+            next_circuit = trotterize(self.pool_qubit_op, alphas_dict, trotter_order=1, n_trotter_steps=1)
+
+            self.circuit_list.append(next_circuit)
+            self.final_circuit += next_circuit
+
+            if self.use_statevector:
+                self.update_statevector(self.backend, self.circuit_list[self.iteration])
+
+            new_energy = self.energy_expectation(self.backend)
+            self.energies.append(new_energy)
+
+            if abs(new_energy - self.final_energy) < self.min_de and self.iteration > 1:
+                self.final_energy = new_energy
+                break
+            self.final_energy = new_energy
+
+        if self.verbose:
+            print(f"Final energy of QITE is {self.final_energy}")
+
+        return self.energies[-1]
+
+    def update_statevector(self, backend: Simulator, next_circuit: Circuit):
+        r"""Update self.final_statevector by propagating with next_circuit using backend
+
+        Args:
+            Simulator: the backend to use for the statevector update
+            Circuit: The circuit to apply to the statevector
+        """
+        _, self.final_statevector = backend.simulate(next_circuit,
+                                                     return_statevector=True,
+                                                     initial_statevector=self.final_statevector)
+
+    def calculate_matrices(self, backend: Simulator, new_energy: float):
+        r"""Calculated matrix elements for imaginary time evolution.
+        The matrices are defined in section 3.5 of https://arxiv.org/pdf/2108.04413.pdf
+
+        Args:
+            backend (Simulator): the backend from which the matrices are generated
+            new_energy (float): the current energy_expectation of the Hamiltonian
+
+        Returns:
+            matrix float: The expectation values <\psi| pu^+ pv |\psi>
+            array float: The expecation values <\psi| pu^+ H |\psi>
+        """
+
+        circuit = Circuit(n_qubits=self.final_circuit.width) if self.use_statevector else self.final_circuit
+
+        ndeltab = np.sqrt(1 - 2 * self.dt * new_energy)
+        prefac = -1j/ndeltab
+        bu = [prefac*backend.get_expectation_value(element, circuit, initial_statevector=self.final_statevector)
+              for element in self.pool_h]
+        bu = np.array(bu)
+        pool_size = len(self.pool_h)
+        suv = np.zeros((pool_size, pool_size), dtype=complex)
+        for u in range(pool_size):
+            for v in range(u+1, pool_size):
+                suv[u, v] = backend.get_expectation_value(self.pool_pool[u][v],
+                                                          circuit,
+                                                          initial_statevector=self.final_statevector)
+                suv[v, u] = suv[u, v]
+
+        return suv, bu
+
+    def energy_expectation(self, backend: Simulator):
+        """Estimate energy using the self.final_circuit, qubit hamiltonian and compute
+        backend.
+
+        Args:
+             backend (Simulator): the backend one computes the energy expectation with
+
+        Returns:
+            float: energy computed by the backend
+        """
+        circuit = Circuit(n_qubits=self.final_circuit.width) if self.use_statevector else self.final_circuit
+        energy = backend.get_expectation_value(self.qubit_hamiltonian.to_qubitoperator(),
+                                               circuit,
+                                               initial_statevector=self.final_statevector)
+        return energy
+
+    def get_resources(self):
+        """Returns resources currently used in underlying state preparation i.e. self.final_circuit
+        the number of pool operators, and the size of qubit_hamiltonian
+
+        Returns:
+            dict: Dictionary of various quantum resources required"""
+        resources = dict()
+        resources["qubit_hamiltonian_terms"] = len(self.qubit_hamiltonian.terms)
+        resources["pool_size"] = len(self.pool_operators)
+        resources["circuit_width"] = self.final_circuit.width
+        resources["circuit_gates"] = self.final_circuit.size
+        resources["circuit_2qubit_gates"] = self.final_circuit.counts.get("CNOT", 0)
+        return resources

tangelo/algorithms/projective/tests/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2021 Good Chemistry Company.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.