catalyst.while_loop

while_loop(cond_fn, allow_array_resizing: bool = False)

A qjit() compatible while-loop decorator for PennyLane/Catalyst.

This decorator provides a functional version of the traditional while loop, similar to jax.lax.while_loop. That is, any variables that are modified across iterations need to be provided as inputs and outputs to the loop body function:

• Input arguments contain the value of a variable at the start of an iteration

• Output arguments contain the value at the end of the iteration. The outputs are then fed back as inputs to the next iteration.

The final iteration values are also returned from the transformed function.

This form of control flow can also be called from the Python interpreter without needing to use qjit().

The semantics of while_loop are given by the following Python pseudo-code:

def while_loop(cond_fun, body_fun, *args):
    while cond_fun(*args):
        args = body_fn(*args)
    return args

Parameters

• cond_fn (Callable) – the condition function in the while loop

• allow_array_resizing (bool) – Whether to allow arrays to change shape/size within the loop. By default this is False; this will allow out-of-scope dynamically-shaped arrays to be captured by the loop, and binary operations to be applied to arrays of the same shape. Set this to True to modify dimension sizes within the loop, however outer-scope dynamically-shaped arrays will no longer be captured, and arrays of the same shape cannot be used in binary operations.

Returns

A wrapper around the while-loop function.

Return type

Callable

Raises

TypeError – Invalid return type of the condition expression.

Example

dev = qml.device("lightning.qubit", wires=1)

@qjit
@qml.qnode(dev)
def circuit(x: float):

    @while_loop(lambda x: x < 2.0)
    def loop_rx(x):
        # perform some work and update (some of) the arguments
        qml.RX(x, wires=0)
        return x ** 2

    # apply the while loop
    final_x = loop_rx(x)

    return qml.expval(qml.PauliZ(0)), final_x

>>> circuit(1.6)
[array(-0.02919952), array(2.56)]

By default, allow_array_resizing is False, allowing dynamically-shaped arrays from outside the for loop to be correctly captured, and arrays of the same shape to be used in binary operations:

>>> @qjit(abstracted_axes={0: 'n'})
... def g(x, y):
...     @catalyst.while_loop(lambda i: jnp.sum(i) > 2., allow_array_resizing=False)
...     def loop(a):
...         # Attempt to capture `x` from the outer scope,
...         # and apply a binary operation '*' between the two arrays.
...         return a * x
...     return loop(y)
>>> x = jnp.array([0.1, 0.2, 0.3])
>>> y = jnp.array([5.2, 10.3, 2.4])
>>> g(x, y)
array([0.052, 0.412, 0.216])

However, if you wish to have the for loop return differently sized arrays at each iteration, set allow_array_resizing to True:

>>> @qjit
... def f(N):
...     a0 = jnp.ones([N])
...     b0 = jnp.ones([N])
...     @while_loop(lambda _a, _b, i: i < 3, allow_array_resizing=True)
...     def loop(a, _, i):
...         i += 1
...         b = jnp.ones([i + 1])
...         return (a, b, i) # return array of new dimensions
...     return loop(a0, b0, 0)
>>> f(2)
(array([1., 1.]), array([1., 1., 1., 1.]), array(3))

Note that when allow_array_resizing=True, dynamically-shaped arrays can no longer be captured from outer-scopes by the for loop, and binary operations between arrays of the same size are not supported.

For more details on dynamically-shaped arrays, please see Dynamically-shaped arrays.

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