qml.gradients.batch_vjp

batch_vjp(tapes, dys, gradient_fn, reduction='append', gradient_kwargs=None)[source]

Generate the gradient tapes and processing function required to compute the vector-Jacobian products of a batch of tapes.

Consider a function \(\mathbf{f}(\mathbf{x})\). The Jacobian is given by

\[\begin{split}\mathbf{J}_{\mathbf{f}}(\mathbf{x}) = \begin{pmatrix} \frac{\partial f_1}{\partial x_1} &\cdots &\frac{\partial f_1}{\partial x_n}\\ \vdots &\ddots &\vdots\\ \frac{\partial f_m}{\partial x_1} &\cdots &\frac{\partial f_m}{\partial x_n}\\ \end{pmatrix}.\end{split}\]

During backpropagation, the chain rule is applied. For example, consider the cost function \(h = y\circ f: \mathbb{R}^n \rightarrow \mathbb{R}\), where \(y: \mathbb{R}^m \rightarrow \mathbb{R}\). The gradient is:

\[\nabla h(\mathbf{x}) = \frac{\partial y}{\partial \mathbf{f}} \frac{\partial \mathbf{f}}{\partial \mathbf{x}} = \frac{\partial y}{\partial \mathbf{f}} \mathbf{J}_{\mathbf{f}}(\mathbf{x}).\]

Denote \(d\mathbf{y} = \frac{\partial y}{\partial \mathbf{f}}\); we can write this in the form of a matrix multiplication:

\[\left[\nabla h(\mathbf{x})\right]_{j} = \sum_{i=0}^m d\mathbf{y}_i ~ \mathbf{J}_{ij}.\]

Thus, we can see that the gradient of the cost function is given by the so-called vector-Jacobian product; the product of the row-vector \(d\mathbf{y}\), representing the gradient of subsequent components of the cost function, and \(\mathbf{J}\), the Jacobian of the current node of interest.

Parameters
  • tapes (Sequence[QuantumTape]) – sequence of quantum tapes to differentiate

  • dys (Sequence[tensor_like]) – Sequence of gradient-output vectors dy. Must be the same length as tapes. Each dy tensor should have shape matching the output shape of the corresponding tape.

  • gradient_fn (callable) – the gradient transform to use to differentiate the tapes

  • reduction (str) – Determines how the vector-Jacobian products are returned. If append, then the output of the function will be of the form List[tensor_like], with each element corresponding to the VJP of each input tape. If extend, then the output VJPs will be concatenated.

  • gradient_kwargs (dict) – dictionary of keyword arguments to pass when determining the gradients of tapes

Returns

list of vector-Jacobian products. None elements corresponds to tapes with no trainable parameters.

Return type

List[tensor_like or None]

Example

Consider the following Torch-compatible quantum tapes:

x = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], requires_grad=True, dtype=torch.float64)

def ansatz(x):
    qml.RX(x[0, 0], wires=0)
    qml.RY(x[0, 1], wires=1)
    qml.RZ(x[0, 2], wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RX(x[1, 0], wires=1)
    qml.RY(x[1, 1], wires=0)
    qml.RZ(x[1, 2], wires=1)

with qml.tape.QuantumTape() as tape1:
    ansatz(x)
    qml.expval(qml.PauliZ(0))
    qml.probs(wires=1)

with qml.tape.QuantumTape() as tape2:
    ansatz(x)
    qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

tapes = [tape1, tape2]

Both tapes share the same circuit ansatz, but have different measurement outputs.

We can use the batch_vjp function to compute the vector-Jacobian product, given a list of gradient-output vectors dys per tape:

>>> dys = [torch.tensor([1., 1., 1.], dtype=torch.float64),
...  torch.tensor([1.], dtype=torch.float64)]
>>> vjp_tapes, fn = qml.gradients.batch_vjp(tapes, dys, qml.gradients.param_shift)

Note that each dy has shape matching the output dimension of the tape (tape1 has 1 expectation and 2 probability values — 3 outputs — and tape2 has 1 expectation value).

Executing the VJP tapes, and applying the processing function:

>>> dev = qml.device("default.qubit", wires=2)
>>> vjps = fn(qml.execute(vjp_tapes, dev, gradient_fn=qml.gradients.param_shift, interface="torch"))
>>> vjps
[tensor([-1.1562e-01, -1.3862e-02, -9.0841e-03, -1.3878e-16, -4.8217e-01,
          2.1329e-17], dtype=torch.float64, grad_fn=<ViewBackward>),
 tensor([ 1.7393e-01, -1.6412e-01, -5.3983e-03, -2.9366e-01, -4.0083e-01,
          2.1134e-17], dtype=torch.float64, grad_fn=<ViewBackward>)]

We have two VJPs; one per tape. Each one corresponds to the number of parameters on the tapes (6).

The output VJPs are also differentiable with respect to the tape parameters:

>>> cost = torch.sum(vjps[0] + vjps[1])
>>> cost.backward()
>>> x.grad
tensor([[-0.4792, -0.9086, -0.2420],
        [-0.0930, -1.0772,  0.0000]], dtype=torch.float64)