def test_tf_gate_wrapper_gate_inheritance_parametrized(g):
inst = g(0.01)(q(0))
wrapped = tf_gate_wrapper(inst)
unitary = cirq.unitary(inst)
wrapped = tf_gate_wrapper(inst, tf.complex64)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor)
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)
def test_tf_gate_wrapper_tensor_inputs():
# TODO
init_t = np.pi / 2
t = tf.Variable(init_t)
inst = cirq.YPowGate(exponent=t)(q(0))
wrapped = tf_gate_wrapper(inst)
print(wrapped._tensor)
def test_tf_gate_wrapper_parity_gate():
for g in [cirq.ZZPowGate]:
inst = g(exponent=3.84)(q(0), q(1))
wrapped = tf_gate_wrapper(inst)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor).reshape((4, 4))
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)
def test_tf_gate_wrapper_gate_inheritance(g):
"""Wrapping a gate instance with a wrapper base class."""
inst = g(q(0))
wrapped = tf_gate_wrapper(inst, tf.complex64)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor)
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)
def simulate_sweep(
self,
program: Union[circuits.Circuit],
params: study.Sweepable,
qubit_order: ops.QubitOrderOrList = ops.QubitOrder.DEFAULT,
initial_state: Any = None) -> List[tf.Tensor]:
"""Simulates the supplied Circuit or Schedule.
Note: This differs from cirq.WaveFunctionSimulator in the return
type, which is List[tf.tensor] instead of List[SimulationTrialResult].
This method returns a result which allows access to the entire
wave function. In contrast to simulate, this allows for sweeping
over different parameter values.
Args:
program: The circuit or schedule to simulate.
params: Parameters to run with the program.
qubit_order: Determines the canonical ordering of the qubits. This
is often used in specifying the initial state, i.e. the
ordering of the computational basis states.
initial_state: The initial state for the simulation. The form of
this state depends on the simulation implementation. See
documentation of the implementing class for details.
Returns:
List of wavefunctions for this run, one for each possible parameter
resolver.
"""
# 1. checking/promotion of initial state to track qubits
# TODO: enforce sizing on initial_state; don't want any stray/missing qubits
if initial_state is None:
initial_state = np.zeros((2**len(program.all_qubits()), ))
initial_state[0] = 1
if isinstance(initial_state, np.ndarray):
initial_qubits = range(int(np.log2(initial_state.shape[0])))
# enforce tensor shape 2,2,2,2,...
initial_state = initial_state.reshape((2, ) * len(initial_qubits))
state = tf.convert_to_tensor(value=initial_state,
dtype=self._dtype)
# fixme: util here:
# state = TFWaveFunctionSimulatorState(state, initial_qubits)
# TODO: gather a set of all qubits acted on in this circuit
# FIXME: initialize buffer variable
# buf = tf.Variable(tf.zeros_like(state, dtype=self._dtype), name='buffer')
# Moment-wise construction of a set of matrices to apply wall
self.ops = []
self.indices = []
for moment in program:
# FIXME: empty moment?
for op in moment.operations:
self.ops.append(tf_gate_wrapper(op, dtype=self._dtype))
self.indices.append([q.x for q in op.qubits])
param_resolvers = study.to_resolvers(params)
trial_results = []
qubit_order = ops.QubitOrder.as_qubit_order(qubit_order)
for param_resolver in param_resolvers:
# prepare to iteratively construct graph down the line of ops
state_and_buff = _StateAndBuffer(state, None) # initializer
for op, inds in zip(self.ops, self.indices):
# can't flush the buffer!!
# buf = tf.assign(tf.get_variable(name='buffer'), tf.zeros_like(state, dtype=self._dtype))
buf = tf.zeros_like(state, dtype=self._dtype)
state_and_buff = _StateAndBuffer(state_and_buff.state, buf)
state_and_buff = self._simulate_unitary(
op, state_and_buff, inds)
# TODO: actually make sweep usable by supplying parameters here..?
# trial_results.append(
# SimulationTrialResult(
# params=param_resolver,
# measurements={},
# final_simulator_state=state_and_buff.state))
trial_results.append(state_and_buff.state)
return trial_results
def test_tf_gate_wrapper_single_qubit_eigengate(g):
inst = g(exponent=3.84)(q(0), q(1))
wrapped = tf_gate_wrapper(inst)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor).reshape((4, 4))
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)
def test_tf_gate_wrapper_single_qubit_eigengate(g):
inst = g(exponent=1.5)(q(0))
wrapped = tf_gate_wrapper(inst, tf.complex64)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor)
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)
def test_tf_gate_wrapper_gate_inheritance(g):
inst = g(q(0), q(1))
wrapped = tf_gate_wrapper(inst, tf.complex64)
with tf.Session() as sess:
tf_inst = sess.run(wrapped._tensor).reshape((4, 4))
np.testing.assert_array_almost_equal(cirq.unitary(inst), tf_inst)