def test_compute_samples_displays(dtype):
a, b, c = cirq.LineQubit.range(3)
circuit = cirq.Circuit(
cirq.X(a),
cirq.H(b),
cirq.X(c),
cirq.H(c),
cirq.approx_pauli_string_expectation(cirq.PauliString({c: cirq.X}),
num_samples=10,
key='approx_x3'),
cirq.approx_pauli_string_expectation(cirq.PauliString({
a: cirq.Z,
b: cirq.X
}),
num_samples=10,
key='approx_z1x2'),
cirq.approx_pauli_string_expectation(cirq.PauliString({
a: cirq.Z,
c: cirq.X
}),
num_samples=10,
key='approx_z1x3'),
)
simulator = cirq.Simulator(dtype=dtype)
result = simulator.compute_samples_displays(circuit)
np.testing.assert_allclose(result.display_values['approx_x3'],
-1,
atol=1e-7)
np.testing.assert_allclose(result.display_values['approx_z1x2'],
-1,
atol=1e-7)
np.testing.assert_allclose(result.display_values['approx_z1x3'],
1,
atol=1e-7)
def test_approx_pauli_string_expectation_helper():
qubits = cirq.LineQubit.range(9)
qubit_pauli_map = {q: cirq.Pauli.by_index(q.x) for q in qubits}
pauli_string = cirq.PauliString(qubit_pauli_map, -1)
assert (cirq.approx_pauli_string_expectation(
pauli_string, num_samples=5,
key='a') == cirq.approx_pauli_string_expectation(pauli_string,
num_samples=5,
key='a'))
def test_with_qubits():
old_qubits = cirq.LineQubit.range(9)
new_qubits = cirq.LineQubit.range(9, 18)
qubit_pauli_map = {q: cirq.Pauli.by_index(q.x) for q in old_qubits}
pauli_string = cirq.PauliString(qubit_pauli_map, -1)
assert (cirq.approx_pauli_string_expectation(
pauli_string, num_samples=1).with_qubits(
*new_qubits) == cirq.approx_pauli_string_expectation(
pauli_string.with_qubits(*new_qubits), num_samples=1))
def hhl_circuit(A, C, t, register_size, *input_prep_gates):
"""
Constructs the HHL circuit.
A is the input Hermitian matrix.
C and t are tunable parameters for the algorithm.
register_size is the size of the eigenvalue register.
input_prep_gates is a list of gates to be applied to |0> to generate the
desired input state |b>.
"""
ancilla = cirq.GridQubit(0, 0)
# to store eigenvalues of the matrix
register = [cirq.GridQubit(i + 1, 0) for i in range(register_size)]
# to store input and output vectors
memory = cirq.GridQubit(register_size + 1, 0)
c = cirq.Circuit()
hs = HamiltonianSimulation(A, t)
pe = PhaseEstimation(register_size + 1, hs)
c.append([gate(memory) for gate in input_prep_gates])
c.append([
pe(*(register + [memory])),
EigenRotation(register_size + 1, C, t)(*(register + [ancilla])),
pe(*(register + [memory]))**-1,
cirq.measure(ancilla)
])
# Pauli observable display
c.append([
cirq.approx_pauli_string_expectation(cirq.PauliString(
{ancilla: cirq.Z}),
num_samples=5000,
key='a'),
cirq.approx_pauli_string_expectation(cirq.PauliString({memory:
cirq.X}),
num_samples=5000,
key='x'),
cirq.approx_pauli_string_expectation(cirq.PauliString({memory:
cirq.Y}),
num_samples=5000,
key='y'),
cirq.approx_pauli_string_expectation(cirq.PauliString({memory:
cirq.Z}),
num_samples=5000,
key='z'),
])
return c
def test_approx_pauli_string_expectation_value_with_coef(
measurements, value, coefficient):
display = cirq.approx_pauli_string_expectation(cirq.PauliString(
{}, coefficient=coefficient),
num_samples=1)
assert display.value_derived_from_samples(
measurements) == value * coefficient
def test_properties():
qubits = cirq.LineQubit.range(9)
qubit_pauli_map = {q: cirq.Pauli.by_index(q.x) for q in qubits}
pauli_string = cirq.PauliString(qubit_pauli_map, -1)
approx_pauli_string_expectation = cirq.approx_pauli_string_expectation(
pauli_string, num_samples=5, key='a')
assert approx_pauli_string_expectation.qubits == tuple(qubits)
assert approx_pauli_string_expectation.num_samples == 5
assert approx_pauli_string_expectation.key == 'a'
def test_approx_pauli_string_expectation_measurement_basis_change(paulis):
qubits = cirq.LineQubit.range(2)
qubit_map = {qubits[0]: paulis[0], qubits[1]: paulis[1]}
display = cirq.approx_pauli_string_expectation(cirq.PauliString(qubit_map),
num_samples=1)
matrix = np.kron(cirq.unitary(paulis[0]), cirq.unitary(paulis[1]))
circuit = cirq.Circuit.from_ops(display.measurement_basis_change())
unitary = circuit.unitary(qubit_order=qubits)
ZZ = np.diag([1, -1, -1, 1])
np.testing.assert_allclose(
np.dot(unitary, np.dot(matrix, unitary.T.conj())), ZZ)
def test_approx_pauli_string_expectation_value(measurements, value):
display = cirq.approx_pauli_string_expectation(cirq.PauliString({}),
num_samples=1)
assert display.value_derived_from_samples(measurements) == value