Get an op that calculates the unitary matrix for the given circuits.

unitary_op = tfq.get_unitary_op()
qubit = cirq.GridQubit(0, 0)
symbol = sympy.Symbol('alpha')
my_circuit = cirq.Circuit(cirq.H(qubit) ** symbol)
tensor_circuit = tfq.convert_to_tensor([my_circuit])
unitary_op(tensor_circuit, ['alpha'], [[0.2]])
<tf.RaggedTensor [
    [[(0.9720+0.0860j), (0.0675-0.2078j)],
     [(0.0675-0.2078j), (0.8369+0.5017j)]]]>

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.

A callable with the following signature:

op(programs, symbol_names, symbol_values)

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, , ] that holds the unitary matrix for each circuit (after resolving the corresponding parameters in).