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Get a TensorFlow op that will calculate sampled expectation values.

Used in the notebooks

Used in the tutorials

This function produces a non-differentiable TF op that will calculate batches of expectation values given tensor batches of cirq.Circuits, parameter values, and cirq.PauliSum operators to measure. Expectation is estimated by taking num_samples shots per term in the corresponding PauliSum.

# Simulate circuits with C++.
my_op = tfq.get_sampled_expectation_op()
# Prepare some inputs.
qubit = cirq.GridQubit(0, 0)
my_symbol = sympy.Symbol('alpha')
my_circuit_tensor = tfq.convert_to_tensor([
    cirq.Circuit(cirq.H(qubit) ** my_symbol)
my_values = np.array([[0.123]])
my_paulis = tfq.convert_to_tensor([[
    3.5 * cirq.X(qubit) - 2.2 * cirq.Y(qubit)
my_num_samples = np.array([[100]])
# This op can now be run with:
output = my_op(
    my_circuit_tensor, ['alpha'], my_values, my_paulis, my_num_samples)
tf.Tensor([[0.71530885]], shape=(1, 1), dtype=float32)

In order to make the op differentiable, a tfq.differentiator object is needed. see tfq.differentiators for more details. Below is a simple example of how to make my_op from the above code block differentiable:

diff = tfq.differentiators.ForwardDifference()
my_differentiable_op = diff.generate_differentiable_op(

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++ sampled expectation op is returned.

A callable with the following signature:

op(programs, symbol_names, symbol_values, pauli_sums, 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.
pauli_sums tf.Tensor of strings with shape [batch_size, n_ops] containing the string representation of the operators that will be used on all of the circuits in the expectation calculations.
num_samples tf.Tensor with num_samples[i][j] is equal to the number of samples to draw in each term of pauli_sums[i][j] when estimating the expectation. Therefore, num_samples must have the same shape as pauli_sums.
Returns tf.Tensor with shape [batch_size, n_ops] that holds the expectation value for each circuit with each op applied to it (after resolving the corresponding parameters in).