qml.gradients.spsa_grad

spsa_grad(tape, argnum=None, h=1e-05, approx_order=2, n=1, strategy='center', f0=None, validate_params=True, num_directions=1, sampler=<function _rademacher_sampler>, sampler_rng=None)

Transform a circuit to compute the SPSA gradient of all gate parameters with respect to its inputs. This estimator shifts all parameters simultaneously and approximates the gradient based on these shifts and a finite-difference method.

Parameters

• tape (QNode or QuantumTape) – quantum circuit to differentiate

• argnum (int or list[int] or None) – Trainable parameter indices to differentiate with respect to. If not provided, the derivatives with respect to all trainable parameters are returned. Note that the indices are with respect to the list of trainable parameters.

• h (float or tensor_like[float]) – Step size for the finite-difference method underlying the SPSA. Can be a tensor-like object with as many entries as differentiated gate parameters

• approx_order (int) – The approximation order of the finite-difference method underlying the SPSA gradient.

• n (int) – compute the \(n\)-th derivative

• strategy (str) – The strategy of the underlying finite difference method. Must be one of "forward", "center", or "backward". For the "forward" strategy, the finite-difference shifts occur at the points \(x_0, x_0+h, x_0+2h,\dots\), where \(h\) is the stepsize h. The "backwards" strategy is similar, but in reverse: \(x_0, x_0-h, x_0-2h, \dots\). Finally, the "center" strategy results in shifts symmetric around the unshifted point: \(\dots, x_0-2h, x_0-h, x_0, x_0+h, x_0+2h,\dots\).

• f0 (tensor_like[float] or None) – Output of the evaluated input tape in tape. If provided, and the gradient recipe contains an unshifted term, this value is used, saving a quantum evaluation.

• validate_params (bool) – Whether to validate the tape parameters or not. If True, the Operation.grad_method attribute and the circuit structure will be analyzed to determine if the trainable parameters support the finite-difference method, inferring that they support SPSA as well. If False, the SPSA gradient method will be applied to all parameters without checking.

• num_directions (int) – Number of sampled simultaneous perturbation vectors. An estimate for the gradient is computed for each vector using the underlying finite-difference method, and afterwards all estimates are averaged.

• sampler (callable) –

  Sampling method to obtain the simultaneous perturbation directions. The sampler should take the following arguments:

  • A Sequence[int] that contains the indices of those trainable tape parameters that will be perturbed, i.e. have non-zero entries in the output vector.

  • An int that indicates the total number of trainable tape parameters. The size of the output vector has to match this input.

  • An int indicating the iteration counter during the gradient estimation. A valid sampling method can, but does not have to, take this counter into account. In any case, sampler has to accept this third argument.

  • The required keyword argument rng, expected to be a NumPy pseudo-random number generator, which should be used to sample directions randomly.

  Note that the circuit evaluations in the various sampled directions are averaged, not simply summed up.

• sampler_rng (Union[np.random.Generator, int, None]) – Either a NumPy pseudo-random number generator or an integer, which will be used as the PRNG seed. Default is None, which creates a NumPy PRNG without a seed. Note that calling spsa_grad multiple times with a seed (i.e., an integer) will result in the same directions being sampled in each call. In this case it is advisable to create a NumPy PRNG and pass it to spsa_grad in each call.

Returns

The transformed circuit as described in qml.transform. Executing this circuit will provide the Jacobian in the form of a tensor, a tuple, or a nested tuple depending upon the nesting structure of measurements in the original circuit.

Return type

qnode (QNode) or tuple[List[QuantumTape], function]

Example

This gradient transform can be applied directly to QNode objects:

>>> @qml.qnode(dev)
... def circuit(params):
...     qml.RX(params[0], wires=0)
...     qml.RY(params[1], wires=0)
...     qml.RX(params[2], wires=0)
...     return qml.expval(qml.Z(0)), qml.var(qml.Z(0))
>>> params = np.array([0.1, 0.2, 0.3], requires_grad=True)
>>> qml.gradients.spsa_grad(circuit)(params)
(tensor([ 0.18488771, -0.18488771, -0.18488771], requires_grad=True),
 tensor([-0.33357922,  0.33357922,  0.33357922], requires_grad=True))

Note that the SPSA gradient is a statistical estimator that uses a given number of function evaluations that does not depend on the number of parameters. While this bounds the cost of the estimation, it also implies that the returned values are not exact (even with shots=None) and that they will fluctuate. See the usage details below for more information.

Usage Details

The number of directions in which the derivative is computed to estimate the gradient can be controlled with the keyword argument num_directions. For the QNode above, a more precise gradient estimation from num_directions=20 directions yields

>>> qml.gradients.spsa_grad(circuit, num_directions=20)(params)
(tensor([-0.53976776, -0.34385475, -0.46106048], requires_grad=True),
 tensor([0.97386303, 0.62039169, 0.83185731], requires_grad=True))

We may compare this to the more precise values obtained from finite differences:

>>> qml.gradients.finite_diff(circuit)(params)
(tensor([-0.38751724, -0.18884792, -0.38355708], requires_grad=True),
 tensor([0.69916868, 0.34072432, 0.69202365], requires_grad=True))

As we can see, the SPSA output is a rather coarse approximation to the true gradient, and this although the parameter-shift rule for three parameters uses just six circuit evaluations, much fewer than SPSA! Consequentially, SPSA is not necessarily useful for small circuits with few parameters, but will pay off for large circuits where other gradient estimators require unfeasibly many circuit executions.

This quantum gradient transform can also be applied to low-level QuantumTape objects. This will result in no implicit quantum device evaluation. Instead, the processed tapes, and post-processing function, which together define the gradient are directly returned:

>>> ops = [qml.RX(params[0], 0), qml.RY(params[1], 0), qml.RX(params[2], 0)]
>>> measurements = [qml.expval(qml.Z(0)), qml.var(qml.Z(0))]
>>> tape = qml.tape.QuantumTape(ops, measurements)
>>> gradient_tapes, fn = qml.gradients.spsa_grad(tape)
>>> gradient_tapes
[<QuantumScript: wires=[0], params=3>, <QuantumScript: wires=[0], params=3>]

This can be useful if the underlying circuits representing the gradient computation need to be analyzed. Here we see that for num_directions=1, the default, we obtain two tapes.

Note that argnum refers to the index of a parameter within the list of trainable parameters. For example, if we have:

>>> tape = qml.tape.QuantumScript(
...     [qml.RX(1.2, wires=0), qml.RY(2.3, wires=0), qml.RZ(3.4, wires=0)],
...     [qml.expval(qml.Z(0))],
...     trainable_params = [1, 2]
... )
>>> qml.gradients.spsa_grad(tape, argnum=1)

The code above will differentiate the third parameter rather than the second.

The output tapes can then be evaluated and post-processed to retrieve the gradient:

>>> dev = qml.device("default.qubit")
>>> fn(qml.execute(gradient_tapes, dev, None))
((tensor(0.18488771, requires_grad=True),
  tensor(-0.18488771, requires_grad=True),
  tensor(-0.18488771, requires_grad=True)),
 (tensor(-0.33357922, requires_grad=True),
  tensor(0.33357922, requires_grad=True),
  tensor(0.33357922, requires_grad=True)))

This gradient transform is compatible with devices that use shot vectors for execution.

>>> shots = (10, 100, 1000)
>>> dev = qml.device("default.qubit", shots=shots)
>>> @qml.qnode(dev)
... def circuit(params):
...     qml.RX(params[0], wires=0)
...     qml.RY(params[1], wires=0)
...     qml.RX(params[2], wires=0)
...     return qml.expval(qml.Z(0)), qml.var(qml.Z(0))
>>> params = np.array([0.1, 0.2, 0.3], requires_grad=True)
>>> qml.gradients.spsa_grad(circuit, h=1e-2)(params)
((array([ 10.,  10., -10.]), array([-18., -18.,  18.])),
 (array([-5., -5.,  5.]), array([ 8.9,  8.9, -8.9])),
 (array([ 1.5,  1.5, -1.5]), array([-2.667, -2.667,  2.667])))

The outermost tuple contains results corresponding to each element of the shot vector, as is also visible by the increasing precision. Note that the stochastic approximation and the fluctuations from the shot noise of the device accumulate, leading to a very coarse-grained estimate for the gradient.

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