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Get a TensorFlow op that produces states from given quantum circuits.
tfq.get_state_op(
backend=None,
*,
quantum_concurrent=quantum_context.get_quantum_concurrent_op_mode()
)
This function produces a non-differentiable op that will calculate
batches of state tensors given tensor batches of cirq.Circuit
s and
parameter values.
# Simulate circuits with cirq.
my_op = tfq.get_state_op(backend=cirq.DensityMatrixSimulator())
# Simulate circuits with C++.
my_second_op = tfq.get_state_op()
# Prepare some inputs.
qubit = cirq.GridQubit(0, 0)
my_symbol = sympy.Symbol('alpha')
my_circuit_tensor = tfq.convert_to_tensor([
cirq.Circuit(cirq.Y(qubit) ** my_symbol)
])
my_values = np.array([[0.5]])
# This op can now be run to calculate the state.
output = my_second_op(my_circuit_tensor, ['alpha'], my_values)
output
<tf.RaggedTensor [[(0.5+0.5j), (0.5+0.5j)]]>
Args | |
---|---|
backend
|
Optional Python object that specifies what backend this op
should use when evaluating circuits. Can be any
cirq.SimulatesFinalState . If not provided, the default C++
state vector simulator will be used.
|
quantum_concurrent
|
Optional Python bool . True indicates that the
returned op should not block graph level parallelism on itself when
executing. False indicates that graph level parallelism on itself
should be blocked. Defaults to value specified in
tfq.get_quantum_concurrent_op_mode which defaults to True
(no blocking). This flag is only needed for advanced users when
using TFQ for very large simulations, or when running on a real
chip.
|
Returns | |
---|---|
A callable with the following signature:
|
|
programs
|
tf.Tensor of strings with shape [batch_size] containing
the string representations of the circuits to be executed.
|
symbol_names
|
tf.Tensor of strings with shape [n_params], which
is used to specify the order in which the values in
symbol_values should be placed inside of the circuits in
programs .
|
symbol_values
|
tf.Tensor of real numbers with shape
[batch_size, n_params] specifying parameter values to resolve
into the circuits specified by programs, following the ordering
dictated by symbol_names .
|
Returns
|
tf.Tensor with shape [batch_size, |