View source on GitHub

Get a Tensorflow op that produces samples from given quantum circuits.

This function produces a non-differentiable op that will calculate batches of circuit samples given tensor batches of cirq.Circuits, parameter values, and a scalar telling the op how many samples to take.

# Simulate circuits with cirq.
my_op = tfq.get_sampling_op(backend=cirq.sim.Simulator())
# Simulate circuits with C++.
my_second_op = tfq.get_sampling_op()
# Prepare some inputs.
qubit = cirq.GridQubit(0, 0)
my_symbol = sympy.Symbol('alpha')
my_circuit_tensor = tfq.convert_to_tensor(
my_values = np.array([[2.0]])
n_samples = np.array([10])
# This op can now be run to take samples.
output = my_second_op(
    my_circuit_tensor, ['alpha'], my_values, n_samples)
<tf.RaggedTensor [[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]]>

backend Optional Python object that specifies what backend this op should use when evaluating circuits. Can be any cirq.Sampler. If not provided the default C++ sampling op is returned.

A callable with the following signature:

op(programs, symbol_names, symbol_values, num_samples)

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.
num_samples tf.Tensor with one element indicating the number of samples to draw.
Returns tf.Tensor with shape [batch_size, num_samples, n_qubits] that holds samples (as boolean values) for each circuit.