tfq.differentiators.LinearCombination

Differentiate a circuit with respect to its inputs by linearly combining values obtained by evaluating the op using parameter values perturbed about their forward-pass values.

Inherits From: Differentiator

tfq.differentiators.LinearCombination(
    weights, perturbations
)

my_op = tfq.get_expectation_op()
weights = [5, 6, 7]
perturbations = [0, 0.5, 0.25]
linear_differentiator = tfq.differentiators.LinearCombination(
   weights, perturbations
)
# Get an expectation op, with this differentiator attached.
op = linear_differentiator.generate_differentiable_op(
    analytic_op=my_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:
    g.watch(symbol_values_t)
    expectations = op(circuit, symbol_names, symbol_values_t, psums
)
# Gradient would be: 5 * f(x+0) + 6 * f(x+0.5) + 7 * f(x+0.25)
grads = g.gradient(expectations, symbol_values_t)
# Note: this gradient visn't correct in value, but showcases
# the principle of how gradients can be defined in a very flexible
# fashion.
grads
tf.Tensor([[5.089467]], shape=(1, 1), dtype=float32)

Args
weights	Python list of real numbers representing linear combination coeffecients for each perturbed function evaluation.
perturbations	Python list of real numbers representing perturbation values.

Methods

differentiate_analytic

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@tf.function
differentiate_analytic(
    programs, symbol_names, symbol_values, pauli_sums, forward_pass_vals, grad
)

Differentiate a circuit with analytical expectation.

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

Note: the default implementation does not use forward_pass_vals; the inheriting differentiator is free to override the default implementation and use this argument if desired.

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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@tf.function
differentiate_sampled(
    programs,
    symbol_names,
    symbol_values,
    pauli_sums,
    num_samples,
    forward_pass_vals,
    grad
)

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.

Note: the default implementation does not use forward_pass_vals; the inheriting differentiator is free to override the default implementation and use this argument if desired.

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.

Caution:

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.

get_gradient_circuits

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@tf.function
get_gradient_circuits(
    programs, symbol_names, symbol_values
)

See base class description.

refresh

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refresh()

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