def test_state_vector_trial_result_repr():
q0 = cirq.NamedQubit('a')
args = cirq.ActOnStateVectorArgs(
target_tensor=np.array([0, 1], dtype=np.int32),
available_buffer=np.array([0, 1], dtype=np.int32),
prng=np.random.RandomState(0),
log_of_measurement_results={},
qubits=[q0],
)
final_step_result = cirq.SparseSimulatorStep(args, cirq.Simulator())
trial_result = cirq.StateVectorTrialResult(
params=cirq.ParamResolver({'s': 1}),
measurements={'m': np.array([[1]], dtype=np.int32)},
final_step_result=final_step_result,
)
expected_repr = (
"cirq.StateVectorTrialResult("
"params=cirq.ParamResolver({'s': 1}), "
"measurements={'m': np.array([[1]], dtype=np.int32)}, "
"final_step_result=cirq.SparseSimulatorStep("
"sim_state=cirq.ActOnStateVectorArgs("
"target_tensor=np.array([0, 1], dtype=np.int32), "
"available_buffer=np.array([0, 1], dtype=np.int32), "
"qubits=(cirq.NamedQubit('a'),), "
"log_of_measurement_results={}), "
"dtype=np.complex64))"
)
assert repr(trial_result) == expected_repr
assert eval(expected_repr) == trial_result
def test_pretty_print():
args = cirq.ActOnStateVectorArgs(
target_tensor=np.array([1]),
available_buffer=np.array([1]),
prng=np.random.RandomState(0),
log_of_measurement_results={},
qubits=[],
)
final_step_result = cirq.SparseSimulatorStep(args, cirq.Simulator())
result = cirq.StateVectorTrialResult(cirq.ParamResolver(), {}, final_step_result)
# Test Jupyter console output from
class FakePrinter:
def __init__(self):
self.text_pretty = ''
def text(self, to_print):
self.text_pretty += to_print
p = FakePrinter()
result._repr_pretty_(p, False)
assert p.text_pretty == 'measurements: (no measurements)\n\nphase:\noutput vector: |⟩'
# Test cycle handling
p = FakePrinter()
result._repr_pretty_(p, True)
assert p.text_pretty == 'StateVectorTrialResult(...)'
def test_simulator_step_state_mixin():
qubits = cirq.LineQubit.range(2)
qubit_map = {qubits[i]: i for i in range(2)}
result = cirq.SparseSimulatorStep(measurements={'m': np.array([1, 2])},
state_vector=np.array([0, 1, 0, 0]),
qubit_map=qubit_map,
dtype=np.complex64)
rho = np.array([[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
np.testing.assert_array_almost_equal(rho, result.density_matrix_of(qubits))
bloch = np.array([0, 0, -1])
np.testing.assert_array_almost_equal(bloch,
result.bloch_vector_of(qubits[1]))
assert result.dirac_notation() == '|01⟩'
def test_str_big():
qs = cirq.LineQubit.range(20)
args = cirq.ActOnStateVectorArgs(
target_tensor=np.array([1] * 2 ** 10),
available_buffer=np.array([1] * 2 ** 10),
prng=np.random.RandomState(0),
log_of_measurement_results={},
qubits=qs,
)
final_step_result = cirq.SparseSimulatorStep(args, cirq.Simulator())
result = cirq.StateVectorTrialResult(
cirq.ParamResolver(),
{},
final_step_result,
)
assert 'output vector: [1 1 1 ..' in str(result)
def test_str_big():
qs = cirq.LineQubit.range(10)
args = cirq.ActOnStateVectorArgs(
prng=np.random.RandomState(0),
log_of_measurement_results={},
qubits=qs,
initial_state=np.array([1] * 2**10, dtype=np.complex64) * 0.03125,
dtype=np.complex64,
)
final_step_result = cirq.SparseSimulatorStep(args, cirq.Simulator())
result = cirq.StateVectorTrialResult(
cirq.ParamResolver(),
{},
final_step_result,
)
assert 'output vector: [0.03125+0.j 0.03125+0.j 0.03125+0.j ..' in str(
result)
def test_simulator_step_state_mixin():
qubits = cirq.LineQubit.range(2)
args = cirq.ActOnStateVectorArgs(
log_of_measurement_results={'m': np.array([1, 2])},
target_tensor=np.array([0, 1, 0, 0]).reshape((2, 2)),
available_buffer=np.array([0, 1, 0, 0]).reshape((2, 2)),
prng=cirq.value.parse_random_state(0),
qubits=qubits,
)
result = cirq.SparseSimulatorStep(
sim_state=args,
dtype=np.complex64,
simulator=None, # type: ignore
)
rho = np.array([[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
np.testing.assert_array_almost_equal(rho, result.density_matrix_of(qubits))
bloch = np.array([0, 0, -1])
np.testing.assert_array_almost_equal(bloch, result.bloch_vector_of(qubits[1]))
assert result.dirac_notation() == '|01⟩'
