def test_quantum_state_computational_basis_state():
state = cirq.quantum_state(7, qid_shape=(3, 4))
np.testing.assert_allclose(
state.data, cirq.one_hot(index=7, shape=(12, ), dtype=np.complex64))
assert state.qid_shape == (3, 4)
assert state.dtype == np.complex64
state = cirq.quantum_state((0, 1, 2, 3),
qid_shape=(1, 2, 3, 4),
dtype=np.complex128)
np.testing.assert_allclose(
state.data,
cirq.one_hot(index=(0, 1, 2, 3),
shape=(1, 2, 3, 4),
dtype=np.complex64))
assert state.qid_shape == (1, 2, 3, 4)
assert state.dtype == np.complex128
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.quantum_state(7)
with pytest.raises(ValueError, match='out of range'):
_ = cirq.quantum_state(7, qid_shape=(2, 2))
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.quantum_state((0, 1, 2, 3))
with pytest.raises(ValueError, match='out of bounds'):
_ = cirq.quantum_state((0, 1, 2, 3), qid_shape=(2, 2, 2, 2))
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.quantum_state((0, 0, 1, 1), qid_shape=(1, 1, 2, 2))
def test_reset_act_on():
with pytest.raises(TypeError, match="Failed to act"):
cirq.act_on(cirq.ResetChannel(), object())
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(1, 1, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
axes=[1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(cirq.ResetChannel(), args)
assert args.log_of_measurement_results == {}
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 0, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
)
cirq.act_on(cirq.ResetChannel(), args)
assert args.log_of_measurement_results == {}
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 0, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
)
def test_reset_act_on():
with pytest.raises(TypeError, match="Failed to act"):
cirq.act_on(cirq.ResetChannel(), DummyActOnArgs(), qubits=())
args = cirq.ActOnStateVectorArgs(
available_buffer=np.empty(shape=(2, 2, 2, 2, 2), dtype=np.complex64),
qubits=cirq.LineQubit.range(5),
prng=np.random.RandomState(),
log_of_measurement_results={},
initial_state=cirq.one_hot(index=(1, 1, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(cirq.ResetChannel(), args, [cirq.LineQubit(1)])
assert args.log_of_measurement_results == {}
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 0, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
)
cirq.act_on(cirq.ResetChannel(), args, [cirq.LineQubit(1)])
assert args.log_of_measurement_results == {}
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 0, 1, 1, 1),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
)
def _kraus_(self):
bottom_right = cirq.one_hot(index=(3, 3),
shape=(4, 4),
dtype=np.complex64)
top_right = cirq.one_hot(index=(0, 3),
shape=(4, 4),
dtype=np.complex64)
return [
np.eye(4) * np.sqrt(3 / 4),
(np.eye(4) - bottom_right) * np.sqrt(1 / 4),
top_right * np.sqrt(1 / 4),
]
def test_to_valid_density_matrix_from_density_matrix_tensor():
np.testing.assert_almost_equal(
cirq.to_valid_density_matrix(cirq.one_hot(shape=(2, 2, 2, 2, 2, 2),
dtype=np.complex64),
num_qubits=3),
cirq.one_hot(shape=(8, 8), dtype=np.complex64),
)
np.testing.assert_almost_equal(
cirq.to_valid_density_matrix(cirq.one_hot(shape=(2, 3, 4, 2, 3, 4),
dtype=np.complex64),
qid_shape=(2, 3, 4)),
cirq.one_hot(shape=(24, 24), dtype=np.complex64),
)
def test_one_hot():
result = cirq.one_hot(shape=4, dtype=np.int32)
assert result.dtype == np.int32
np.testing.assert_array_equal(result, [1, 0, 0, 0])
np.testing.assert_array_equal(
cirq.one_hot(shape=[2, 3], dtype=np.complex64), [[1, 0, 0], [0, 0, 0]])
np.testing.assert_array_equal(
cirq.one_hot(shape=[2, 3], dtype=np.complex64, index=(0, 2)),
[[0, 0, 1], [0, 0, 0]])
np.testing.assert_array_equal(
cirq.one_hot(shape=5, dtype=np.complex128, index=3), [0, 0, 0, 1, 0])
def test_quantum_state():
state_vector_1 = cirq.one_hot(shape=(4,), dtype=np.complex128)
state_tensor_1 = np.reshape(state_vector_1, (2, 2))
density_matrix_1 = np.outer(state_vector_1, np.conj(state_vector_1))
state = cirq.QuantumState(state_vector_1, qid_shape=(2, 2))
assert state.data is state_vector_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex128
np.testing.assert_array_equal(state.state_vector(), state_vector_1)
np.testing.assert_array_equal(state.state_tensor(), state_tensor_1)
np.testing.assert_array_equal(state.density_matrix(), density_matrix_1)
np.testing.assert_array_equal(state.state_vector_or_density_matrix(), state_vector_1)
state = cirq.QuantumState(state_tensor_1, qid_shape=(2, 2))
assert state.data is state_tensor_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex128
np.testing.assert_array_equal(state.state_vector(), state_vector_1)
np.testing.assert_array_equal(state.state_tensor(), state_tensor_1)
np.testing.assert_array_equal(state.density_matrix(), density_matrix_1)
np.testing.assert_array_equal(state.state_vector_or_density_matrix(), state_vector_1)
state = cirq.QuantumState(density_matrix_1, qid_shape=(2, 2))
assert state.data is density_matrix_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex128
assert state.state_vector() is None
assert state.state_tensor() is None
np.testing.assert_array_equal(state.density_matrix(), density_matrix_1)
np.testing.assert_array_equal(state.state_vector_or_density_matrix(), density_matrix_1)
def test_with_registers():
circuit = quirk_url_to_circuit(
'https://algassert.com/quirk#circuit={"cols":'
'['
'[{"id":"setA","arg":3}],'
'["+=AB3",1,1,"inputB2"]'
']}')
op = cast(cirq.ArithmeticOperation, circuit[0].operations[0])
with pytest.raises(ValueError, match='number of registers'):
_ = op.with_registers()
with pytest.raises(ValueError, match='first register.*mutable target'):
_ = op.with_registers(1, 2, 3)
op2 = op.with_registers([], 5, 5)
np.testing.assert_allclose(cirq.unitary(cirq.Circuit(op2)),
np.array([[1]]),
atol=1e-8)
op2 = op.with_registers([*cirq.LineQubit.range(3)], 5, 5)
np.testing.assert_allclose(cirq.final_state_vector(cirq.Circuit(op2),
initial_state=0),
cirq.one_hot(index=25 % 8,
shape=8,
dtype=np.complex64),
atol=1e-8)
def test_decomposed_fallback():
class Composite(cirq.Gate):
def num_qubits(self) -> int:
return 1
def _decompose_(self, qubits):
yield cirq.X(*qubits)
qid_shape = (2, )
tensor = cirq.to_valid_density_matrix(0,
len(qid_shape),
qid_shape=qid_shape,
dtype=np.complex64)
args = cirq.ActOnDensityMatrixArgs(
target_tensor=tensor,
available_buffer=[np.empty_like(tensor) for _ in range(3)],
qubits=cirq.LineQubit.range(1),
prng=np.random.RandomState(),
log_of_measurement_results={},
qid_shape=qid_shape,
)
cirq.act_on(Composite(), args, cirq.LineQubit.range(1))
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 1), shape=(2, 2), dtype=np.complex64))
def test_act_on_state_vector():
a, b = [cirq.LineQubit(3), cirq.LineQubit(1)]
m = cirq.measure(a,
b,
key='out',
invert_mask=(True, ),
confusion_map={(1, ): np.array([[0, 1], [1, 0]])})
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
qubits=cirq.LineQubit.range(5),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(shape=(2, 2, 2, 2, 2), dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [1, 1]}
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
qubits=cirq.LineQubit.range(5),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(index=(0, 1, 0, 0, 0),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [1, 0]}
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
qubits=cirq.LineQubit.range(5),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(index=(0, 1, 0, 1, 0),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
datastore = cast(cirq.ClassicalDataDictionaryStore, args.classical_data)
out = cirq.MeasurementKey('out')
assert args.log_of_measurement_results == {'out': [0, 0]}
assert datastore.records[out] == [(0, 0)]
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [0, 0]}
assert datastore.records[out] == [(0, 0), (0, 0)]
def test_act_on_qutrit():
a, b = [cirq.LineQid(3, dimension=3), cirq.LineQid(1, dimension=3)]
m = cirq.measure(
a,
b,
key='out',
invert_mask=(True, ),
confusion_map={(1, ): np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]])},
)
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
qubits=cirq.LineQid.range(5, dimension=3),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(index=(0, 2, 0, 2, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [2, 0]}
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
qubits=cirq.LineQid.range(5, dimension=3),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(index=(0, 1, 0, 2, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [2, 2]}
args = cirq.StateVectorSimulationState(
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
qubits=cirq.LineQid.range(5, dimension=3),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(index=(0, 2, 0, 1, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [0, 0]}
def test_act_on_gate():
args = ccq.mps_simulator.MPSState(qubits=cirq.LineQubit.range(3),
prng=np.random.RandomState(0))
cirq.act_on(cirq.X, args, [cirq.LineQubit(1)])
np.testing.assert_allclose(
args.state_vector().reshape((2, 2, 2)),
cirq.one_hot(index=(0, 1, 0), shape=(2, 2, 2), dtype=np.complex64),
)
def test_act_on():
a, b = cirq.LineQubit.range(2)
m = cirq.measure(a, b, key='out', invert_mask=(True, ))
with pytest.raises(TypeError, match="Failed to act"):
cirq.act_on(m, object())
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(shape=(2, 2, 2, 2, 2), dtype=np.complex64),
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [1, 0]}
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(0, 1, 0, 0, 0),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [1, 1]}
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(0, 1, 0, 1, 0),
shape=(2, 2, 2, 2, 2),
dtype=np.complex64),
available_buffer=np.empty(shape=(2, 2, 2, 2, 2)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [0, 1]}
with pytest.raises(ValueError, match="already logged to key"):
cirq.act_on(m, args)
def test_decomposed_fallback():
class Composite(cirq.Gate):
def num_qubits(self) -> int:
return 1
def _decompose_(self, qubits):
yield cirq.X(*qubits)
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64),
available_buffer=np.empty((2, 2, 2), dtype=np.complex64),
axes=[1],
prng=np.random.RandomState(),
log_of_measurement_results={})
cirq.act_on(Composite(), args)
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(0, 1, 0), shape=(2, 2, 2), dtype=np.complex64))
def test_decomposed_fallback():
class Composite(cirq.Gate):
def num_qubits(self) -> int:
return 1
def _decompose_(self, qubits):
yield cirq.X(*qubits)
args = cirq.StateVectorSimulationState(
available_buffer=np.empty((2, 2, 2), dtype=np.complex64),
qubits=cirq.LineQubit.range(3),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64),
dtype=np.complex64,
)
cirq.act_on(Composite(), args, [cirq.LineQubit(1)])
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(0, 1, 0), shape=(2, 2, 2), dtype=np.complex64))
def test_density_matrix():
density_matrix_1 = np.eye(4, dtype=np.complex64) / 4
state_vector_1 = cirq.one_hot(shape=(4,), dtype=np.complex64)
state = cirq.density_matrix(density_matrix_1)
assert state.data is density_matrix_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex64
with pytest.raises(ValueError, match='square'):
_ = cirq.density_matrix(state_vector_1)
def test_default_parameter():
dtype = np.complex64
tensor = cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64)
qubits = cirq.LineQubit.range(3)
args = cirq.StateVectorSimulationState(qubits=qubits,
initial_state=tensor,
dtype=dtype)
qid_shape = cirq.protocols.qid_shape(qubits)
tensor = np.reshape(tensor, qid_shape)
np.testing.assert_almost_equal(args.target_tensor, tensor)
assert args.available_buffer.shape == tensor.shape
assert args.available_buffer.dtype == tensor.dtype
def test_quantum_state_state_vector_state_tensor():
state_vector_1 = cirq.one_hot(shape=(4,), dtype=np.complex128)
state_tensor_1 = np.reshape(state_vector_1, (2, 2))
state = cirq.quantum_state(state_vector_1, dtype=np.complex64, qid_shape=(2, 2))
np.testing.assert_array_equal(state.data, state_vector_1)
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex64
state = cirq.quantum_state(state_tensor_1, qid_shape=(2, 2))
assert state.data is state_tensor_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex128
def test_cannot_act():
class NoDetails:
pass
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64),
available_buffer=np.empty((2, 2, 2), dtype=np.complex64),
axes=[1],
prng=np.random.RandomState(),
log_of_measurement_results={})
with pytest.raises(TypeError, match="Failed to act"):
cirq.act_on(NoDetails(), args)
def test_act_on_qutrit():
a, b = cirq.LineQid.range(2, dimension=3)
m = cirq.measure(a, b, key='out', invert_mask=(True, ))
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(0, 2, 0, 2, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [2, 2]}
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(0, 1, 0, 2, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [2, 1]}
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(index=(0, 2, 0, 1, 0),
shape=(3, 3, 3, 3, 3),
dtype=np.complex64),
available_buffer=np.empty(shape=(3, 3, 3, 3, 3)),
axes=[3, 1],
prng=np.random.RandomState(),
log_of_measurement_results={},
)
cirq.act_on(m, args)
assert args.log_of_measurement_results == {'out': [0, 2]}
def test_cannot_act():
class NoDetails:
pass
args = cirq.ActOnStateVectorArgs(
target_tensor=cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64),
available_buffer=np.empty((2, 2, 2), dtype=np.complex64),
qubits=cirq.LineQubit.range(3),
prng=np.random.RandomState(),
log_of_measurement_results={},
)
with pytest.raises(TypeError, match="Can't simulate operations"):
cirq.act_on(NoDetails(), args, qubits=())
def test_cannot_act():
class NoDetails:
pass
args = cirq.StateVectorSimulationState(
available_buffer=np.empty((2, 2, 2), dtype=np.complex64),
qubits=cirq.LineQubit.range(3),
prng=np.random.RandomState(),
initial_state=cirq.one_hot(shape=(2, 2, 2), dtype=np.complex64),
dtype=np.complex64,
)
with pytest.raises(TypeError, match="Can't simulate operations"):
cirq.act_on(NoDetails(), args, qubits=())
def test_monte_carlo_on_unknown_channel():
class Reset11To00(cirq.Gate):
def num_qubits(self) -> int:
return 2
def _kraus_(self):
return [
np.eye(4) - cirq.one_hot(index=(3, 3), shape=(4, 4), dtype=np.complex64),
cirq.one_hot(index=(0, 3), shape=(4, 4), dtype=np.complex64),
]
for k in range(4):
out = cirq.Simulator().simulate(
cirq.Circuit(Reset11To00().on(*cirq.LineQubit.range(2))), initial_state=k
)
np.testing.assert_allclose(
out.state_vector(), cirq.one_hot(index=k % 3, shape=4, dtype=np.complex64), atol=1e-8
)
def test_quantum_state_state_vector_state_tensor():
state_vector_1 = cirq.one_hot(shape=(4, ), dtype=np.complex128)
state_tensor_1 = np.reshape(state_vector_1, (2, 2))
state = cirq.quantum_state(state_vector_1, dtype=np.complex64)
np.testing.assert_array_equal(state.data, state_vector_1)
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex64
state = cirq.quantum_state(state_tensor_1, qid_shape=(2, 2))
assert state.data is state_tensor_1
assert state.qid_shape == (2, 2)
assert state.dtype == np.complex128
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.quantum_state(state_tensor_1)
with pytest.raises(ValueError, match='not compatible'):
_ = cirq.quantum_state(state_tensor_1, qid_shape=(2, 3))
def test_decomposed_fallback():
class Composite(cirq.Gate):
def num_qubits(self) -> int:
return 1
def _decompose_(self, qubits):
yield cirq.X(*qubits)
args = cirq.DensityMatrixSimulationState(
qubits=cirq.LineQubit.range(1),
prng=np.random.RandomState(),
initial_state=0,
dtype=np.complex64,
)
cirq.act_on(Composite(), args, cirq.LineQubit.range(1))
np.testing.assert_allclose(
args.target_tensor,
cirq.one_hot(index=(1, 1), shape=(2, 2), dtype=np.complex64))
def test_quantum_state_quantum_state():
state_vector_1 = cirq.one_hot(shape=(4,), dtype=np.complex128)
quantum_state = cirq.QuantumState(state_vector_1, qid_shape=(2, 2))
state = cirq.quantum_state(quantum_state)
assert state is quantum_state
assert state.data is quantum_state.data
assert state.dtype == np.complex128
state = cirq.quantum_state(quantum_state, copy=True)
assert state is not quantum_state
assert state.data is not quantum_state.data
assert state.dtype == np.complex128
state = cirq.quantum_state(quantum_state, dtype=np.complex64)
assert state is not quantum_state
assert state.data is not quantum_state.data
assert state.dtype == np.complex64
with pytest.raises(ValueError, match='qid shape'):
state = cirq.quantum_state(quantum_state, qid_shape=(4,))
def test_infer_qid_shape():
computational_basis_state_1 = [0, 0, 0, 1]
computational_basis_state_2 = [0, 1, 2, 3]
computational_basis_state_3 = [0, 1, 2, 4]
computational_basis_state_4 = 9
computational_basis_state_5 = [0, 1, 2, 4, 5]
state_vector_1 = cirq.one_hot(shape=(4, ), dtype=np.complex64)
state_vector_2 = cirq.one_hot(shape=(24, ), dtype=np.complex64)
state_tensor_1 = np.reshape(state_vector_1, (2, 2))
state_tensor_2 = np.reshape(state_vector_2, (1, 2, 3, 4))
density_matrix_1 = np.eye(4, dtype=np.complex64) / 4
density_matrix_2 = np.eye(24, dtype=np.complex64) / 24
q0, q1 = cirq.LineQubit.range(2)
product_state_1 = cirq.KET_PLUS(q0) * cirq.KET_PLUS(q1)
assert cirq.qis.infer_qid_shape(
computational_basis_state_1,
state_vector_1,
state_tensor_1,
density_matrix_1,
product_state_1,
) == (2, 2)
assert cirq.qis.infer_qid_shape(
product_state_1,
density_matrix_1,
state_tensor_1,
state_vector_1,
computational_basis_state_1,
) == (2, 2)
assert cirq.qis.infer_qid_shape(
computational_basis_state_1,
computational_basis_state_2,
computational_basis_state_4,
state_tensor_2,
) == (1, 2, 3, 4)
assert cirq.qis.infer_qid_shape(state_vector_2, density_matrix_2,
computational_basis_state_4) == (24, )
assert cirq.qis.infer_qid_shape(state_tensor_2,
density_matrix_2) == (1, 2, 3, 4)
assert cirq.qis.infer_qid_shape(computational_basis_state_4) == (10, )
assert cirq.qis.infer_qid_shape(15, 7, 22, 4) == (23, )
with pytest.raises(ValueError, match='No states were specified'):
_ = cirq.qis.infer_qid_shape()
with pytest.raises(ValueError, match='Failed'):
_ = cirq.qis.infer_qid_shape(computational_basis_state_1,
computational_basis_state_5)
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.qis.infer_qid_shape(computational_basis_state_1)
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.qis.infer_qid_shape(state_tensor_1)
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.qis.infer_qid_shape(density_matrix_1)
with pytest.raises(ValueError, match='ambiguous'):
_ = cirq.qis.infer_qid_shape(computational_basis_state_1,
computational_basis_state_2)
with pytest.raises(ValueError, match='Failed'):
_ = cirq.qis.infer_qid_shape(state_vector_1,
computational_basis_state_4)
with pytest.raises(ValueError, match='Failed to infer'):
_ = cirq.qis.infer_qid_shape(state_vector_1, state_vector_2)
with pytest.raises(ValueError, match='Failed to infer'):
_ = cirq.qis.infer_qid_shape(computational_basis_state_3,
state_tensor_2)
def test_act_using_adaptive_two_qubit_channel():
class Decay11(cirq.Gate):
def num_qubits(self) -> int:
return 2
def _kraus_(self):
bottom_right = cirq.one_hot(index=(3, 3),
shape=(4, 4),
dtype=np.complex64)
top_right = cirq.one_hot(index=(0, 3),
shape=(4, 4),
dtype=np.complex64)
return [
np.eye(4) * np.sqrt(3 / 4),
(np.eye(4) - bottom_right) * np.sqrt(1 / 4),
top_right * np.sqrt(1 / 4),
]
mock_prng = mock.Mock()
def get_result(state: np.ndarray, sample: float):
mock_prng.random.return_value = sample
args = cirq.ActOnStateVectorArgs(
target_tensor=np.copy(state),
available_buffer=np.empty_like(state),
qubits=cirq.LineQubit.range(4),
prng=mock_prng,
log_of_measurement_results={},
)
cirq.act_on(Decay11(), args, [cirq.LineQubit(1), cirq.LineQubit(3)])
return args.target_tensor
def assert_not_affected(state: np.ndarray, sample: float):
np.testing.assert_allclose(get_result(state, sample), state, atol=1e-8)
all_zeroes = cirq.one_hot(index=(0, 0, 0, 0),
shape=(2, ) * 4,
dtype=np.complex128)
all_ones = cirq.one_hot(index=(1, 1, 1, 1),
shape=(2, ) * 4,
dtype=np.complex128)
decayed_all_ones = cirq.one_hot(index=(1, 0, 1, 0),
shape=(2, ) * 4,
dtype=np.complex128)
# Decays the 11 state to 00.
np.testing.assert_allclose(get_result(all_ones, 3 / 4 - 1e-8), all_ones)
np.testing.assert_allclose(get_result(all_ones, 3 / 4 + 1e-8),
decayed_all_ones)
# Decoheres the 11 subspace from other subspaces as sample rises.
superpose = all_ones * np.sqrt(1 / 2) + all_zeroes * np.sqrt(1 / 2)
np.testing.assert_allclose(get_result(superpose, 3 / 4 - 1e-8), superpose)
np.testing.assert_allclose(get_result(superpose, 3 / 4 + 1e-8), all_zeroes)
np.testing.assert_allclose(get_result(superpose, 7 / 8 - 1e-8), all_zeroes)
np.testing.assert_allclose(get_result(superpose, 7 / 8 + 1e-8),
decayed_all_ones)
# Always acts like identity when sample < p=3/4.
for _ in range(10):
assert_not_affected(
cirq.testing.random_superposition(dim=16).reshape((2, ) * 4),
sample=3 / 4 - 1e-8,
)
# Acts like identity on superpositions of first three states.
for _ in range(10):
mock_prng.random.return_value = 3 / 4 + 1e-6
projected_state = cirq.testing.random_superposition(dim=16).reshape(
(2, ) * 4)
projected_state[cirq.slice_for_qubits_equal_to([1, 3], 3)] = 0
projected_state /= np.linalg.norm(projected_state)
assert abs(np.linalg.norm(projected_state) - 1) < 1e-8
assert_not_affected(
projected_state,
sample=3 / 4 + 1e-8,
)
def test_fidelity_fail_inference():
state_vector = cirq.one_hot(shape=(4,), dtype=np.complex128)
state_tensor = np.reshape(state_vector, (2, 2))
with pytest.raises(ValueError, match='Please specify'):
_ = cirq.fidelity(state_tensor, 4)
def _channel_(self):
return [
np.eye(4) -
cirq.one_hot(index=(3, 3), shape=(4, 4), dtype=np.complex64),
cirq.one_hot(index=(0, 3), shape=(4, 4), dtype=np.complex64),
]