def test_ansatz_circuit_three_layers(self, n_qubits, topology):
# Given
number_of_layers = 3
ansatz = QCBMAnsatz(
number_of_layers=number_of_layers,
number_of_qubits=n_qubits,
topology=topology,
)
params = [
np.random.rand(2 * n_qubits),
np.random.rand(int((n_qubits * (n_qubits - 1)) / 2)),
np.random.rand(2 * n_qubits),
]
expected_circuit = Circuit([
# First layer
*[RX(params[0][i])(i) for i in range(n_qubits)],
*[RZ(params[0][i + n_qubits])(i) for i in range(n_qubits)],
# Second layer
*get_entangling_layer(params[1], n_qubits, XX,
topology).operations,
# Third layer
*[RZ(params[2][i])(i) for i in range(n_qubits)],
*[RX(params[2][i + n_qubits])(i) for i in range(n_qubits)],
])
params = np.concatenate(params)
# When
circuit = ansatz.get_executable_circuit(params)
# Then
assert circuit == expected_circuit
def target_unitary(self, beta, gamma, symbols_map):
target_circuit = Circuit()
target_circuit += H(0)
target_circuit += H(1)
target_circuit += RZ(2 * gamma)(0)
target_circuit += RZ(2 * gamma)(1)
target_circuit += RX(2 * beta)(0)
target_circuit += RX(2 * beta)(1)
return target_circuit.bind(symbols_map).to_unitary()
def time_evolution_for_term(
term: QubitOperator, time: Union[float,
sympy.Expr]) -> circuits.Circuit:
"""Evolves a Pauli term for a given time and returns a circuit representing it.
Based on section 4 from https://arxiv.org/abs/1001.3855 .
Args:
term: Pauli term to be evolved
time: time of evolution
Returns:
Circuit: Circuit representing evolved term.
"""
if len(term.terms) != 1:
raise ValueError("This function works only on a single term.")
term_components = list(term.terms.keys())[0]
base_changes = []
base_reversals = []
cnot_gates = []
central_gate = None
term_types = [component[1] for component in term_components]
qubit_indices = [component[0] for component in term_components]
coefficient = list(term.terms.values())[0]
circuit = circuits.Circuit()
# If constant term, return empty circuit.
if not term_components:
return circuit
for i, (term_type, qubit_id) in enumerate(zip(term_types, qubit_indices)):
if term_type == "X":
base_changes.append(H(qubit_id))
base_reversals.append(H(qubit_id))
elif term_type == "Y":
base_changes.append(RX(np.pi / 2)(qubit_id))
base_reversals.append(RX(-np.pi / 2)(qubit_id))
if i == len(term_components) - 1:
central_gate = RZ(2 * time * coefficient)(qubit_id)
else:
cnot_gates.append(CNOT(qubit_id, qubit_indices[i + 1]))
for gate in base_changes:
circuit += gate
for gate in cnot_gates:
circuit += gate
circuit += central_gate
for gate in reversed(cnot_gates):
circuit += gate
for gate in base_reversals:
circuit += gate
return circuit
def circuits(self):
circuits = [Circuit() for _ in range(5)]
circuits[1] += RX(1.2)(0)
circuits[1] += RY(1.5)(1)
circuits[1] += RX(-0.0002)(0)
circuits[1] += RY(0)(1)
for circuit in circuits[2:]:
circuit += RX(sympy.Symbol("theta_0"))(0)
circuit += RY(sympy.Symbol("theta_1"))(1)
circuit += RX(sympy.Symbol("theta_2"))(0)
circuit += RY(sympy.Symbol("theta_3"))(1)
return circuits
def _validate_expectation_value_includes_coefficients(
estimator: EstimateExpectationValues, ):
estimation_tasks = [
EstimationTask(IsingOperator("Z0"), Circuit([RX(np.pi / 3)(0)]),
10000),
EstimationTask(IsingOperator("Z0", 19.971997),
Circuit([RX(np.pi / 3)(0)]), 10000),
]
expectation_values = estimator(
backend=_backend,
estimation_tasks=estimation_tasks,
)
return not np.array_equal(expectation_values[0].values,
expectation_values[1].values)
def _build_rotational_subcircuit(self, circuit: Circuit,
parameters: np.ndarray) -> Circuit:
"""Add the subcircuit which includes several rotation gates acting on each qubit
Args:
circuit: The circuit to append to
parameters: The variational parameters (or symbolic parameters)
Returns:
circuit with added rotational sub-layer
"""
# Add RZ(theta) RX(pi/2) RZ(theta') RX(pi/2) RZ(theta'')
for qubit_index in range(self.number_of_qubits):
qubit_parameters = parameters[qubit_index * 3:(qubit_index + 1) *
3]
circuit += RZ(qubit_parameters[0])(qubit_index)
circuit += RX(np.pi / 2)(qubit_index)
circuit += RZ(qubit_parameters[1])(qubit_index)
circuit += RX(np.pi / 2)(qubit_index)
circuit += RZ(qubit_parameters[2])(qubit_index)
return circuit
def test_commutes_with_parameter_substitution(self):
theta, gamma = sympy.symbols("theta, gamma")
circuit = Circuit([
RX(theta / 2)(0),
X(1),
RY(gamma / 4).controlled(1)(1, 2),
YY(0.1)(0, 4)
])
symbols_map = {theta: 0.1, gamma: 0.5}
parameterized_unitary = circuit.to_unitary()
unitary = circuit.bind(symbols_map).to_unitary()
np.testing.assert_array_almost_equal(
np.array(parameterized_unitary.subs(symbols_map), dtype=complex),
unitary)
def test_ansatz_circuit_one_layer(self, n_qubits, topology):
# Given
number_of_layers = 1
ansatz = QCBMAnsatz(
number_of_layers=number_of_layers,
number_of_qubits=n_qubits,
topology=topology,
)
params = [np.random.rand(n_qubits)]
expected_circuit = Circuit()
for i in range(n_qubits):
expected_circuit += RX(params[0][i])(i)
params = np.concatenate(params)
# When
circuit = ansatz.get_executable_circuit(params)
# Then
assert circuit == expected_circuit
def test_perform_context_selection(self):
target_operators = []
target_operators.append(10.0 * QubitOperator("Z0"))
target_operators.append(-3 * QubitOperator("Y0"))
target_operators.append(1 * QubitOperator("X0"))
target_operators.append(20 * QubitOperator(""))
expected_operators = []
expected_operators.append(10.0 * QubitOperator("Z0"))
expected_operators.append(-3 * QubitOperator("Z0"))
expected_operators.append(1 * QubitOperator("Z0"))
expected_operators.append(20 * QubitOperator(""))
base_circuit = Circuit([X(0)])
x_term_circuit = Circuit([RY(-np.pi / 2)(0)])
y_term_circuit = Circuit([RX(np.pi / 2)(0)])
expected_circuits = [
base_circuit,
base_circuit + y_term_circuit,
base_circuit + x_term_circuit,
base_circuit,
]
estimation_tasks = [
EstimationTask(operator, base_circuit, None)
for operator in target_operators
]
tasks_with_context_selection = perform_context_selection(
estimation_tasks)
for task, expected_circuit, expected_operator in zip(
tasks_with_context_selection, expected_circuits,
expected_operators):
assert task.operator.terms == expected_operator.terms
assert task.circuit == expected_circuit
class TestCreatingUnitaryFromCircuit:
@pytest.fixture
def cirq_unitaries(self):
"""
Note: We decided to go with file-based approach after extracting cirq
from z-quantum-core and not being able to use `export_to_cirq` anymore.
"""
path_to_array = ("/".join(__file__.split("/")[:-1]) +
"/hardcoded_cirq_unitaries.npy")
return np.load(path_to_array, allow_pickle=True)
@pytest.mark.parametrize(
"circuit, unitary_index",
[
# Identity gates in some test cases below are used so that comparable
# Cirq circuits have the same number of qubits as Zquantum ones.
(Circuit([RX(np.pi / 5)(0)]), 0),
(Circuit([RY(np.pi / 2)(0), RX(np.pi / 5)(0)]), 1),
(
Circuit(
[I(1),
I(2),
I(3),
I(4),
RX(np.pi / 5)(0),
XX(0.1)(5, 0)]),
2,
),
(
Circuit([
XY(np.pi).controlled(1)(3, 1, 4),
RZ(0.1 * np.pi).controlled(2)(0, 2, 1),
]),
3,
),
(
Circuit(
[H(1),
YY(0.1).controlled(1)(0, 1, 2),
X(2),
Y(3),
Z(4)]),
4,
),
],
)
def test_without_free_params_gives_the_same_result_as_cirq(
self, circuit, unitary_index, cirq_unitaries):
zquantum_unitary = circuit.to_unitary()
assert isinstance(
zquantum_unitary, np.ndarray
), "Unitary constructed from non-parameterized circuit is not a numpy array."
np.testing.assert_array_almost_equal(zquantum_unitary,
cirq_unitaries[unitary_index])
def test_commutes_with_parameter_substitution(self):
theta, gamma = sympy.symbols("theta, gamma")
circuit = Circuit([
RX(theta / 2)(0),
X(1),
RY(gamma / 4).controlled(1)(1, 2),
YY(0.1)(0, 4)
])
symbols_map = {theta: 0.1, gamma: 0.5}
parameterized_unitary = circuit.to_unitary()
unitary = circuit.bind(symbols_map).to_unitary()
np.testing.assert_array_almost_equal(
np.array(parameterized_unitary.subs(symbols_map), dtype=complex),
unitary)
import numpy as np
from openfermion import IsingOperator
from zquantum.core.circuits import RX, RY, RZ, Circuit, H
from zquantum.core.interfaces.estimation import (
EstimateExpectationValues,
EstimationTask,
)
from zquantum.core.symbolic_simulator import SymbolicSimulator
_backend = SymbolicSimulator(seed=1997)
_estimation_tasks = [
EstimationTask(IsingOperator("Z0"), Circuit([H(0)]), 10000),
EstimationTask(
IsingOperator("Z0") + IsingOperator("Z1") + IsingOperator("Z2"),
Circuit([H(0), RX(np.pi / 3)(0), H(2)]),
10000,
),
EstimationTask(
IsingOperator("Z0") + IsingOperator("Z1", 4),
Circuit([
RX(np.pi)(0),
RY(0.12)(1),
RZ(np.pi / 3)(1),
RY(1.9213)(0),
]),
10000,
),
]
def test_by_default_only_gate_operations_are_supported():
simulator = SymbolicSimulatorWithDefaultSetOfSupportedOperations()
assert simulator.is_natively_supported(RX(np.pi / 2)(1))
assert not simulator.is_natively_supported(
MultiPhaseOperation((0.5, 0.2, 0.3, 0.1)))
class SymbolicSimulatorWithNonSupportedOperations(SymbolicSimulator):
def is_natively_supported(self, operation: Operation) -> bool:
return super().is_natively_supported(
operation) and operation.gate.name != "RX"
class SymbolicSimulatorWithDefaultSetOfSupportedOperations(SymbolicSimulator):
def is_natively_supported(self, operation: Operation) -> bool:
return QuantumSimulator.is_natively_supported(self, operation)
@pytest.mark.parametrize(
"circuit",
[
Circuit([RY(0.5)(0), RX(1)(1),
CNOT(0, 2), RX(np.pi)(2)]),
Circuit([RX(1)(1), CNOT(0, 2),
RX(np.pi)(2), RY(0.5)(0)]),
Circuit([RX(1)(1),
CNOT(0, 2),
RX(np.pi)(2),
RY(0.5)(0),
RX(0.5)(0)]),
],
)
def test_quantum_simulator_switches_between_native_and_nonnative_modes_of_execution(
circuit, ):
simulator = SymbolicSimulatorWithNonSupportedOperations()
reference_simulator = SymbolicSimulator()
