Differentiate a circuit with respect to its inputs by adjoint method.

Inherits From: Differentiator

The Adjoint differentiator follows along with the methods described here: arXiv:1912.10877 and doi: 10.1111/j.1365-246X.2006.02978.x. The Adjoint method differentiates the input circuits in roughly one forward and backward pass over the circuits, to calculate the gradient of a symbol only a constant number of gate operations need to be applied to the circuits state. When the number of parameters in a circuit is very large, this differentiator performs much better than all the others found in TFQ.

my_op = tfq.get_expectation_op()
adjoint_differentiator = tfq.differentiators.Adjoint()
# Get an expectation op, with this differentiator attached.
op = adjoint_differentiator.generate_differentiable_op(
qubit = cirq.GridQubit(0, 0)
circuit = tfq.convert_to_tensor([
    cirq.Circuit(cirq.X(qubit) ** sympy.Symbol('alpha'))
psums = tfq.convert_to_tensor([[cirq.Z(qubit)]])
symbol_values = np.array([[0.123]], dtype=np.float32)
# Calculate tfq gradient.
symbol_values_t = tf.convert_to_tensor(symbol_values)
symbol_names = tf.convert_to_tensor(['alpha'])
with tf.GradientTape() as g:
    expectations = op(circuit, symbol_names, symbol_values_t, psums
grads = g.gradient(expectations, symbol_values_t)
tf.Tensor([[-1.1839]], shape=(1, 1), dtype=float32)



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Differentiate a circuit with sampled expectation.

This is called at graph runtime by TensorFlow. differentiate_sampled calls he inheriting differentiator's get_gradient_circuits and uses those components to construct the gradient.

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 of positive integers representing the number of samples per term in each term of pauli_sums used during the forward pass.
forward_pass_vals tf.Tensor of real numbers with shape [batch_size, n_ops] containing the output of the forward pass through the op you are differentiating.
grad tf.Tensor of real numbers with shape [batch_size, n_ops] representing the gradient backpropagated to the output of the op you are differentiating through.

A tf.Tensor with the same shape as symbol_values representing the gradient backpropageted to the symbol_values input of the op you are differentiating through.


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Generate a differentiable op by attaching self to an op.

See tfq.differentiators.Differentiator. This has been partially re-implemented by the Adjoint differentiator to disallow the sampled_op input.

sampled_op A callable op that you want to make differentiable using this differentiator's differentiate_sampled method.
analytic_op A callable op that you want to make differentiable using this differentiators differentiate_analytic method.

A callable op that who's gradients are now registered to be a call to this differentiators differentiate_* function.


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See base class description.


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Refresh this differentiator in order to use it with other ops.