def produce_simulation_test_parameters(
hamiltonian: Hamiltonian,
time: float,
seed: Optional[int] = None) -> Tuple[numpy.ndarray, numpy.ndarray]:
"""Produce objects for testing Hamiltonian simulation.
Produces a random initial state and evolves it under the given Hamiltonian
for the specified amount of time. Returns the initial state and final
state.
Args:
hamiltonian: The Hamiltonian to evolve under.
time: The time to evolve for
seed: An RNG seed.
"""
n_qubits = openfermion.count_qubits(hamiltonian)
# Construct a random initial state
initial_state = openfermion.haar_random_vector(2**n_qubits, seed)
# Simulate exact evolution
hamiltonian_sparse = openfermion.get_sparse_operator(hamiltonian)
exact_state = scipy.sparse.linalg.expm_multiply(
-1j * time * hamiltonian_sparse, initial_state)
# Make sure the time is not too small
assert fidelity(exact_state, initial_state) < .95
return initial_state, exact_state
)
circuit = cirq.Circuit.from_ops(inverse_basis_rotation)
# Add diagonal phase rotations to circuit
for k, eigenvalue in enumerate(eigenvalues):
phase = -eigenvalue * simulation_time
circuit.append(cirq.Rz(rads=phase).on(qubits[k]))
# Finally, change back to the computational basis
basis_rotation = openfermioncirq.bogoliubov_transform(
qubits, basis_transformation_matrix
)
circuit.append(basis_rotation)
# Initialize a random initial state
initial_state = openfermion.haar_random_vector(
2 ** n_qubits, random_seed).astype(numpy.complex64)
# Numerically compute the correct circuit output
hamiltonian_sparse = openfermion.get_sparse_operator(H)
exact_state = scipy.sparse.linalg.expm_multiply(
-1j * simulation_time * hamiltonian_sparse, initial_state
)
# Use Cirq simulator to apply circuit
simulator = cirq.Simulator()
result = simulator.simulate(circuit, qubit_order=qubits,
initial_state=initial_state)
simulated_state = result.final_state
# Print final fidelity
fidelity = abs(numpy.dot(simulated_state, numpy.conjugate(exact_state)))**2
