qjit(fn=None, *, autograph=False, autograph_include=(), async_qnodes=False, target='binary', keep_intermediate=False, verbose=False, logfile=None, pipelines=None, static_argnums=None, abstracted_axes=None)[source]

A just-in-time decorator for PennyLane and JAX programs using Catalyst.

This decorator enables both just-in-time and ahead-of-time compilation, depending on whether function argument type hints are provided.


The supported backend devices are currently lightning.qubit, lightning.kokkos, braket.local.qubit, braket.aws.qubit, and oqc.cloud. For a list of supported operations, observables, and measurements, please see the Quick Start.

  • fn (Callable) – the quantum or classical function

  • autograph (bool) – Experimental support for automatically converting Python control flow statements to Catalyst-compatible control flow. Currently supports Python if, elif, else, and for statements. Note that this feature requires an available TensorFlow installation. For more details, see the AutoGraph guide.

  • autograph_include – A list of (sub)modules to be allow-listed for autograph conversion.

  • async_qnodes (bool) – Experimental support for automatically executing QNodes asynchronously, if supported by the device runtime.

  • target (str) – the compilation target

  • keep_intermediate (bool) – Whether or not to store the intermediate files throughout the compilation. If True, intermediate representations are available via the mlir, jaxpr, and qir, representing different stages in the optimization process.

  • verbosity (bool) – If True, the tools and flags used by Catalyst behind the scenes are printed out.

  • logfile (Optional[TextIOWrapper]) – File object to write verbose messages to (default - sys.stderr).

  • pipelines (Optional(List[Tuple[str,List[str]]])) – A list of pipelines to be executed. The elements of this list are named sequences of MLIR passes to be executed. A None value (the default) results in the execution of the default pipeline. This option is considered to be used by advanced users for low-level debugging purposes.

  • static_argnums (int or Seqence[Int]) – an index or a sequence of indices that specifies the positions of static arguments.

  • abstracted_axes (Sequence[Sequence[str]] or Dict[int, str] or Sequence[Dict[int, str]]) – An experimental option to specify dynamic tensor shapes. This option affects the compilation of the annotated function. Function arguments with abstracted_axes specified will be compiled to ranked tensors with dynamic shapes. For more details, please see the Dynamically-shaped Arrays section below.


QJIT object.

  • FileExistsError – Unable to create temporary directory

  • PermissionError – Problems creating temporary directory

  • OSError – Problems while creating folder for intermediate files

  • AutoGraphError – Raised if there was an issue converting the given the function(s).

  • ImportError – Raised if AutoGraph is turned on and TensorFlow could not be found.


In just-in-time (JIT) mode, the compilation is triggered at the call site the first time the quantum function is executed. For example, circuit is compiled as early as the first call.

@qml.qnode(qml.device("lightning.qubit", wires=2))
def circuit(theta):
    qml.RX(theta, wires=1)
    return qml.expval(qml.PauliZ(wires=1))
>>> circuit(0.5)  # the first call, compilation occurs here
>>> circuit(0.5)  # the precompiled quantum function is called

Alternatively, if argument type hints are provided, compilation can occur ‘ahead of time’ when the function is decorated.

from jax.core import ShapedArray

@qjit  # compilation happens at definition
@qml.qnode(qml.device("lightning.qubit", wires=2))
def circuit(x: complex, z: ShapedArray(shape=(3,), dtype=jnp.float64)):
    theta = jnp.abs(x)
    qml.RY(theta, wires=0)
    qml.Rot(z[0], z[1], z[2], wires=0)
    return qml.state()
>>> circuit(0.2j, jnp.array([0.3, 0.6, 0.9]))  # calls precompiled function
array([0.75634905-0.52801002j, 0. +0.j,
       0.35962678+0.14074839j, 0. +0.j])

For more details on compilation and debugging, please see Sharp bits and debugging tips.


Most decomposition logic will be equivalent to PennyLane’s decomposition. However, decomposition logic will differ in the following cases:

  1. All qml.Controlled operations will decompose

    to qml.QubitUnitary operations.

  2. qml.ControlledQubitUnitary operations will

    decompose to qml.QubitUnitary operations.

  3. The list of device-supported gates employed by Catalyst is currently different than that

    of the lightning.qubit device, as defined by the QJITDevice.

Catalyst also supports capturing imperative Python control flow in compiled programs. You can enable this feature via the autograph=True parameter. Note that it does come with some restrictions, in particular whenever global state is involved. Refer to the AutoGraph guide for a complete discussion of the supported and unsupported use-cases.

@qml.qnode(qml.device("lightning.qubit", wires=2))
def circuit(x: int):

    if x < 5:

    return qml.expval(qml.PauliZ(0))
>>> circuit(3)
>>> circuit(5)

Note that imperative control flow will still work in Catalyst even when the AutoGraph feature is turned off, it just won’t be captured in the compiled program and cannot involve traced values. The example above would then raise a tracing error, as there is no value for x yet than can be compared in the if statement. A loop like for i in range(5) would be unrolled during tracing, “copy-pasting” the body 5 times into the program rather than appearing as is.

Library code is not meant to be targeted by Autograph conversion, hence pennylane, catalyst and jax modules have been excluded from it. But sometimes it might make sense enabling specific submodules from the excluded modules for which conversion may be appropriate. For these cases one can use the autograph_include parameter, which provides a list of modules/submodules that will always be enabled for conversion no matter if the default conversion rules were excluding them before.

import excluded_module

@qjit(autograph=True, autograph_include=["excluded_module.submodule"])
def g(x: int):
    return excluded_module.submodule.f(x)

Notice that autograph=True must be set in order to process the autograph_include list. Otherwise an error will be reported.

To update array values when using JAX, the JAX syntax for array assignment (which uses the array at and set methods) must be used:

def f(x):
first_dim = x.shape[0]
result = jnp.empty((first_dim,), dtype=x.dtype)

for i in range(first_dim):
    result = result.at[i].set(x[i]* 2)

return result

However, if updating a single index of the array, Autograph supports conversion of standard Python array assignment syntax:

def f(x):
first_dim = x.shape[0]
result = jnp.empty((first_dim,), dtype=x.dtype)

for i in range(first_dim):
    result[i] = x[i] * 2

return result

Under the hood, Catalyst converts anything coming in the latter notation into the former one.

static_argnums defines which elements should be treated as static. If it takes an integer, it means the argument whose index is equal to the integer is static. If it takes an iterable of integers, arguments whose index is contained in the iterable are static. Changing static arguments will introduce re-compilation.

A valid static argument must be hashable and its __hash__ method must be able to reflect any changes of its attributes.

class MyClass:
    val: int

    def __hash__(self):
        return hash(str(self))

def f(
    x: int,
    y: MyClass,
    return x + y.val

f(1, MyClass(5))
f(1, MyClass(6)) # re-compilation
f(2, MyClass(5)) # no re-compilation

In the example above, y is static. Note that the second function call triggers re-compilation since the input object is different from the previous one. However, the third function call direcly uses the previous compiled one and does not introduce re-compilation.

class MyClass:
    val: int

    def __hash__(self):
        return hash(str(self))

@qjit(static_argnums=(1, 2))
def f(
    x: int,
    y: MyClass,
    z: MyClass,
    return x + y.val + z.val

my_obj_1 = MyClass(5)
my_obj_2 = MyClass(6)
f(1, my_obj_1, my_obj_2)
my_obj_1.val = 7
f(1, my_obj_1, my_obj_2) # re-compilation

In the example above, y and z are static. The second function should make function f be re-compiled because my_obj_1 is changed. This requires that the mutation is properly reflected in the hash value.

Note that even when static_argnums is used in conjunction with type hinting, ahead-of-time compilation will not be possible since the static argument values are not yet available. Instead, compilation will be just-in-time.

There are three ways to use abstracted_axes; by passing a sequence of tuples, a dictionary, or a sequence of dictionaries. Passing a sequence of tuples:

abstracted_axes=((), ('n',), ('m', 'n'))

Each tuple in the sequence corresponds to one of the arguments in the annotated function. Empty tuples can be used and correspond to parameters with statically known shapes. Non-empty tuples correspond to parameters with dynamically known shapes.

In this example above,

  • the first argument will have a statically known shape,

  • the second argument has its zeroth axis have dynamic shape n, and

  • the third argument will have its zeroth axis with dynamic shape m and first axis with dynamic shape n.

Passing a dictionary:

abstracted_axes={0: 'n'}

This approach allows a concise expression of the relationships between axes for different function arguments. In this example, it specifies that for all function arguments, the zeroth axis will have dynamic shape n.

Passing a sequence of dictionaries:

abstracted_axes=({}, {0: 'n'}, {1: 'm', 0: 'n'})

The example here is a more verbose version of the tuple example. This convention allows axes to be omitted from the list of abstracted axes.

Using abstracted_axes can help avoid the cost of recompilation. By using abstracted_axes, a more general version of the compiled function will be generated. This more general version is parametrized over the abstracted axes and allows results to be computed over tensors independently of their axes lengths.

For example:

def sum(arr):
    return jnp.sum(arr)

sum(jnp.array([1]))     # Compilation happens here.
sum(jnp.array([1, 1]))  # And here!

The sum function would recompile each time an array of different size is passed as an argument.

@qjit(abstracted_axes={0: "n"})
def sum_abstracted(arr):
    return jnp.sum(arr)

sum(jnp.array([1]))     # Compilation happens here.
sum(jnp.array([1, 1]))  # No need to recompile.

the sum_abstracted function would only compile once and its definition would be reused for subsequent function calls.