tfq.differentiators.Differentiator

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Interface that defines how to specify gradients for a quantum circuit.

Used in the notebooks

Used in the tutorials
Calculate gradients

This abstract class allows for the creation of gradient calculation procedures for (expectation values from) quantum circuits, with respect to a set of input parameter values. This allows one to backpropagate through a quantum circuit.

Methods

differentiate_analytic

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@abc.abstractmethod
differentiate_analytic(
    programs, symbol_names, symbol_values, pauli_sums, forward_pass_vals, grad
)

Specify how to differentiate a circuit with analytical expectation.

This is called at graph runtime by TensorFlow. differentiate_analytic should calculate the gradient of a batch of circuits and return it formatted as indicated below. See tfq.differentiators.ForwardDifference for an example.

Args
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.
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.

Returns
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.

differentiate_sampled

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@abc.abstractmethod
differentiate_sampled(
    programs, symbol_names, symbol_values, pauli_sums, num_samples,
    forward_pass_vals, grad
)

Specify how to differentiate a circuit with sampled expectation.

This is called at graph runtime by TensorFlow. differentiate_sampled should calculate the gradient of a batch of circuits and return it formatted as indicated below. See tfq.differentiators.ForwardDifference for an example.

Args
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.

Returns
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.

generate_differentiable_op

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generate_differentiable_op(
    *, sampled_op=None, analytic_op=None
)

Generate a differentiable op by attaching self to an op.

This function returns a tf.function that passes values through to forward_op during the forward pass and this differentiator (self) to backpropagate through the op during the backward pass. If sampled_op is provided the differentiators differentiate_sampled method will be invoked (which requires sampled_op to be a sample based expectation op with num_samples input tensor). If analytic_op is provided the differentiators differentiate_analytic method will be invoked (which requires analytic_op to be an analytic based expectation op that does NOT have num_samples as an input). If both sampled_op and analytic_op are provided an exception will be raised.

This generate_differentiable_op() can be called only ONCE because of the one differentiator per op policy. You need to call refresh() to reuse this differentiator with another op.

Args
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.

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

refresh

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refresh()

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