Catalyst v0.6.0

New features

• Catalyst now supports externally hosted callbacks with parameters and return values within qjit-compiled code. This provides the ability to insert native Python code into any qjit-compiled function, allowing for the capability to include subroutines that do not yet support qjit-compilation and enhancing the debugging experience. (#540) (#596) (#610) (#650) (#649) (#661) (#686) (#689)

  The following two callback functions are available:

  • catalyst.pure_callback supports callbacks of pure functions. That is, functions with no side-effects that accept parameters and return values. However, the return type and shape of the function must be known in advance, and is provided as a type signature.

    @pure_callback
    def callback_fn(x) -> float:
        # here we call non-JAX compatible code, such
        # as standard NumPy
        return np.sin(x)
    
    @qjit
    def fn(x):
        return jnp.cos(callback_fn(x ** 2))

    >>> fn(0.654)
    array(0.9151995)

  • catalyst.debug.callback supports callbacks of functions with no return values. This makes it an easy entry point for debugging, for example via printing or logging at runtime.

    @catalyst.debug.callback
    def callback_fn(y):
        print("Value of y =", y)
    
    @qjit
    def fn(x):
        y = jnp.sin(x)
        callback_fn(y)
        return y ** 2

    >>> fn(0.54)
    Value of y = 0.5141359916531132
    array(0.26433582)
    >>> fn(1.52)
    Value of y = 0.998710143975583
    array(0.99742195)

  Note that callbacks do not currently support differentiation, and cannot be used inside functions that catalyst.grad is applied to.

• More flexible runtime printing through support for format strings. (#621)

  The catalyst.debug.print function has been updated to support Python-like format strings:

  @qjit
  def cir(a, b, c):
      debug.print("{c} {b} {a}", a=a, b=b, c=c)

  >>> cir(1, 2, 3)
  3 2 1

  Note that previous functionality of the print function to print out memory reference information of variables has been moved to catalyst.debug.print_memref.

• Catalyst now supports QNodes that execute on Oxford Quantum Circuits (OQC) superconducting hardware, via OQC Cloud. (#578) (#579) (#691)

  To use OQC Cloud with Catalyst, simply ensure your credentials are set as environment variables, and load the oqc.cloud device to be used within your qjit-compiled workflows.

  import os
  os.environ["OQC_EMAIL"] = "your_email"
  os.environ["OQC_PASSWORD"] = "your_password"
  os.environ["OQC_URL"] = "oqc_url"
  
  dev = qml.device("oqc.cloud", backend="lucy", shots=2012, wires=2)
  
  @qjit
  @qml.qnode(dev)
  def circuit(a: float):
      qml.Hadamard(0)
      qml.CNOT(wires=[0, 1])
      qml.RX(wires=0)
      return qml.counts(wires=[0, 1])
  
  print(circuit(0.2))

• Catalyst now ships with an instrumentation feature allowing to explore what steps are run during compilation and execution, and for how long. (#528) (#597)

  Instrumentation can be enabled from the frontend with the catalyst.debug.instrumentation context manager:

  >>> @qjit
  ... def expensive_function(a, b):
  ...     return a + b
  >>> with debug.instrumentation("session_name", detailed=False):
  ...     expensive_function(1, 2)
  [DIAGNOSTICS] Running capture                   walltime: 3.299 ms      cputime: 3.294 ms       programsize: 0 lines
  [DIAGNOSTICS] Running generate_ir               walltime: 4.228 ms      cputime: 4.225 ms       programsize: 14 lines
  [DIAGNOSTICS] Running compile                   walltime: 57.182 ms     cputime: 12.109 ms      programsize: 121 lines
  [DIAGNOSTICS] Running run                       walltime: 1.075 ms      cputime: 1.072 ms  

  The results will be appended to the provided file if the filename attribute is set, and printed to the console otherwise. The flag detailed determines whether individual steps in the compiler and runtime are instrumented, or whether only high-level steps like "program capture" and "compilation" are reported.

  Measurements currently include wall time, CPU time, and (intermediate) program size.

Improvements

• AutoGraph now supports return statements inside conditionals in qjit-compiled functions. (#583)

  For example, the following pattern is now supported, as long as all return values have the same type:

  @qjit(autograph=True)
  def fn(x):
      if x > 0:
          return jnp.sin(x)
      return jnp.cos(x)

  >>> fn(0.1)
  array(0.09983342)
  >>> fn(-0.1)
  array(0.99500417)

  This support extends to quantum circuits:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def f(x: float):
    qml.RX(x, wires=0)
  
    m = catalyst.measure(0)
  
    if not m:
        return m, qml.expval(qml.PauliZ(0))
  
    qml.RX(x ** 2, wires=0)
  
    return m, qml.expval(qml.PauliZ(0))

  >>> f(1.4)
  (array(False), array(1.))
  >>> f(1.4)
  (array(True), array(0.37945176))

  Note that returning results with different types or shapes within the same function, such as different observables or differently shaped arrays, is not possible.

• Errors are now raised at compile time if the gradient of an unsupported function is requested. (#204)

  At the moment, CompileError exceptions will be raised if at compile time it is found that code reachable from the gradient operation contains either a mid-circuit measurement, a callback, or a JAX-style custom call (which happens through the mitigation operation as well as certain JAX operations).

• Catalyst now supports devices built from the new PennyLane device API. (#565) (#598) (#599) (#636) (#638) (#664) (#687)

  When using the new device API, Catalyst will discard the preprocessing from the original device, replacing it with Catalyst-specific preprocessing based on the TOML file provided by the device. Catalyst also requires that provided devices specify their wires upfront.

• A new compiler optimization that removes redundant chains of self inverse operations has been added. This is done within a new MLIR pass called remove-chained-self-inverse. Currently we only match redundant Hadamard operations, but the list of supported operations can be expanded. (#630)

• The catalyst.measure operation is now more lenient in the accepted type for the wires parameter. In addition to a scalar, a 1D array is also accepted as long as it only contains one element. (#623)

  For example, the following is now supported:

  catalyst.measure(wires=jnp.array([0]))

• The compilation & execution of @qjit compiled functions can now be aborted using an interrupt signal (SIGINT). This includes using CTRL-C from a command line and the Interrupt button in a Jupyter Notebook. (#642)

• The Catalyst Amazon Braket support has been updated to work with the latest version of the Amazon Braket PennyLane plugin (v1.25.0) and Amazon Braket Python SDK (v1.73.3) (#620) (#672) (#673)

  Note that with this update, all declared qubits in a submitted program will always be measured, even if specific qubits were never used.

• An updated quantum device specification format, TOML schema v2, is now supported by Catalyst. This allows device authors to specify properties such as native quantum control support, gate invertibility, and differentiability on a per-operation level. (#554)

  For more details on the new TOML schema, please refer to the custom devices documentation.

• An exception is now raised when OpenBLAS cannot be found by Catalyst during compilation. (#643)

Breaking changes

• qml.sample and qml.counts now produce integer arrays for the sample array and basis state array when used without obser...

Catalyst v0.5.0

New features

• Catalyst now provides a QJIT compatible catalyst.vmap function, which makes it even easier to modify functions to map over inputs with additional batch dimensions. (#497) (#569)

  When working with tensor/array frameworks in Python, it can be important to ensure that code is written to minimize usage of Python for loops (which can be slow and inefficient), and instead push as much of the computation through to the array manipulation library, by taking advantage of extra batch dimensions.

  For example, consider the following QNode:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qml.qnode(dev)
  def circuit(x, y):
      qml.RX(jnp.pi * x[0] + y, wires=0)
      qml.RY(x[1] ** 2, wires=0)
      qml.RX(x[1] * x[2], wires=0)
      return qml.expval(qml.PauliZ(0))

  >>> circuit(jnp.array([0.1, 0.2, 0.3]), jnp.pi)
  Array(-0.93005586, dtype=float64)

  We can use catalyst.vmap to introduce additional batch dimensions to our input arguments, without needing to use a Python for loop:

  >>> x = jnp.array([[0.1, 0.2, 0.3],
  ...                [0.4, 0.5, 0.6],
  ...                [0.7, 0.8, 0.9]])
  >>> y = jnp.array([jnp.pi, jnp.pi / 2, jnp.pi / 4])
  >>> qjit(vmap(cost))(x, y)
  array([-0.93005586, -0.97165424, -0.6987465 ])

  catalyst.vmap() has been implemented to match the same behaviour of jax.vmap, so should be a drop-in replacement in most cases. Under-the-hood, it is automatically inserting Catalyst-compatible for loops, which will be compiled and executed outside of Python for increased performance.

• Catalyst now supports compiling and executing QJIT-compiled QNodes using the CUDA Quantum compiler toolchain. (#477) (#536) (#547)

  Simply import the CUDA Quantum @cudaqjit decorator to use this functionality:

  from catalyst.cuda import cudaqjit

  Or, if using Catalyst from PennyLane, simply specify @qml.qjit(compiler="cuda_quantum").

  The following devices are available when compiling with CUDA Quantum:

  • softwareq.qpp: a modern C++ statevector simulator
  • nvidia.custatevec: The NVIDIA CuStateVec GPU simulator (with support for multi-gpu)
  • nvidia.cutensornet: The NVIDIA CuTensorNet GPU simulator (with support for matrix product state)

  For example:

  dev = qml.device("softwareq.qpp", wires=2)
  
  @cudaqjit
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(x[0], wires=0)
      qml.RY(x[1], wires=1)
      qml.CNOT(wires=[0, 1])
      return qml.expval(qml.PauliY(0))

  >>> circuit(jnp.array([0.5, 1.4]))
  -0.47244976756708373

  Note that CUDA Quantum compilation currently does not have feature parity with Catalyst compilation; in particular, AutoGraph, control flow, differentiation, and various measurement statistics (such as probabilities and variance) are not yet supported. Classical code support is also limited.

• Catalyst now supports just-in-time compilation of static (compile-time constant) arguments. (#476) (#550)

  The @qjit decorator takes a new argument static_argnums, which specifies positional arguments of the decorated function should be treated as compile-time static arguments.

  This allows any hashable Python object to be passed to the function during compilation; the function will only be re-compiled if the hash value of the static arguments change. Otherwise, re-using previous static argument values will result in no re-compilation.

  @qjit(static_argnums=(1,))
  def f(x, y):
      print(f"Compiling with y={y}")
      return x + y

  >>> f(0.5, 0.3)
  Compiling with y=0.3
  array(0.8)
  >>> f(0.1, 0.3)  # no re-compilation occurs
  array(0.4)
  >>> f(0.1, 0.4)  # y changes, re-compilation
  Compiling with y=0.4
  array(0.5)

  This functionality can be used to support passing arbitrary Python objects to QJIT-compiled functions, as long as they are hashable:

  from dataclasses import dataclass
  
  @dataclass
  class MyClass:
      val: int
  
      def __hash__(self):
          return hash(str(self))
  
  @qjit(static_argnums=(1,))
  def f(x: int, y: MyClass):
      return x + y.val

  >>> f(1, MyClass(5))
  array(6)
  >>> f(1, MyClass(6))  # re-compilation
  array(7)
  >>> f(2, MyClass(5))  # no re-compilation
  array(7)

• Mid-circuit measurements now support post-selection and qubit reset when used with the Lightning simulators. (#491) (#507)

  To specify post-selection, simply pass the postselect argument to the catalyst.measure function:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qjit
  @qml.qnode(dev)
  def f():
      qml.Hadamard(0)
      m = measure(0, postselect=1)
      return qml.expval(qml.PauliZ(0))

  Likewise, to reset a wire after mid-circuit measurement, simply specify reset=True:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qjit
  @qml.qnode(dev)
  def f():
      qml.Hadamard(0)
      m = measure(0, reset=True)
      return qml.expval(qml.PauliZ(0))

Improvements

• Catalyst now supports Python 3.12 (#532)

• The JAX version used by Catalyst has been updated to v0.4.23. (#428)

• Catalyst now supports the qml.GlobalPhase operation. (#563)

• Native support for qml.PSWAP and qml.ISWAP gates on Amazon Braket devices has been added. (#458)

  Specifically, a circuit like

  dev = qml.device("braket.local.qubit", wires=2, shots=100)
  
  @qjit
  @qml.qnode(dev)
  def f(x: float):
      qml.Hadamard(0)
      qml.PSWAP(x, wires=[0, 1])
      qml.ISWAP(wires=[1, 0])
      return qml.probs()

  would no longer decompose the PSWAP and ISWAP gates.

• The qml.BlockEncode operator is now supported with Catalyst. (#483)

• Catalyst no longer relies on a TensorFlow installation for its AutoGraph functionality. Instead, the standalone diastatic-malt package is used and automatically installed as a dependency. (#401)

• The @qjit decorator will remember previously compiled functions when the PyTree metadata of arguments changes, in addition to also remembering compiled functions when static arguments change. (#522)

  The following example will no longer trigger a third compilation:

  @qjit
  def func(x):
      print("compiling")
      return x

  >>> func([1,]);             # list
  compiling
  >>> func((2,));             # tuple
  compiling
  >>> func([3,]);             # list

  Note however that in order to keep overheads low, changing the argument type or shape (in a promotion incompatible way) may override a previously stored function (with identical PyTree metadata and static argument values):

  @qjit
  def func(x):
      print("compiling")
      return x

  >>> func(jnp.array(1));     # scalar
  compiling
  >>> func(jnp.array([2.]));  # 1-D array
  compiling
  >>> func(jnp.array(3));     # scalar
  compiling

• Catalyst gradient functions (grad, jacobian, vjp, and jvp) now support being applied to functions that use (nested) container types as inputs and outputs. This includes lists and dictionaries, as well as any data structure implementing the PyTree protocol. (#500) (#501) (#508) (#549)

  dev = qml.device("lightning.qubit", wires=1)
  
  @qml.qnode(dev)
  def circuit(phi, psi):
      qml.RY(phi, wires=0)
      qml.RX(psi, wires=0)
      return [{"expval0": qml.expval(qml.PauliZ(0))}, qml.expval(qml.PauliZ(0))]
  
  psi = 0.1
  phi = 0.2

  >>> qjit(jacobian(circuit, argnum=[0, 1]))(psi, phi)
  [{'expval0': (array(-0.0978434), array(-0.19767681))}, (array(-0.0978434), array(-0.19767681))]

• Support has been added for linear algebra functions which depend on computing the eigenvalues of symmetric matrices, such as np.sqrt_matrix(). (#488)

  For example, you can compile qml.math.sqrt_matrix:

  @qml.qjit
  def workflow(A):
      B = qml.math.sqrt_matrix(A)
      return B @ A

  Internally, this involves support for lowering the eigenvectors/values computation lapack method lapack_dsyevd via stablehlo.custom_call.

• Additional debugging functions are now available in the catalyst.debug directory. (#529) (#522)

  This includes:

  • filter_static_args(args, static_argnums) to remove static values from arguments using the
    provided index list.

  • get_cmain(fn, *args) to return a C program that calls a jitted function w...

Catalyst v0.4.1

Improvements

• Catalyst wheels are now packaged with OpenMP and ZStd, which avoids installing additional requirements separately in order to use pre-packaged Catalyst binaries. (#457) (#478)

  Note that OpenMP support for the lightning.kokkos backend has been disabled on macOS x86_64, due to memory issues in the computation of Lightning's adjoint-jacobian in the presence of multiple OMP threads.

Bug fixes

• Resolve an infinite recursion in the decomposition of the Controlled operator whenever computing a Unitary matrix for the operator fails. (#468)

• Resolve a failure to generate gradient code for specific input circuits. (#439)

  In this case, jnp.mod was used to compute wire values in a for loop, which prevented the gradient architecture from fully separating quantum and classical code. The following program is now supported:

  @qjit
  @grad
  @qml.qnode(dev)
  def f(x):
      def cnot_loop(j):
          qml.CNOT(wires=[j, jnp.mod((j + 1), 4)])
  
      for_loop(0, 4, 1)(cnot_loop)()
  
      return qml.expval(qml.PauliZ(0))

• Resolve unpredictable behaviour when importing libraries that share Catalyst's LLVM dependency (e.g. TensorFlow). In some cases, both packages exporting the same symbols from their shared libraries can lead to process crashes and other unpredictable behaviour, since the wrong functions can be called if both libraries are loaded in the current process. The fix involves building shared libraries with hidden (macOS) or protected (linux) symbol visibility by default, exporting only what is necessary. (#465)

• Resolve a failure to find the SciPy OpenBLAS library when running Catalyst, due to a different SciPy version being used to build Catalyst than to run it. (#471)

• Resolve a memory leak in the runtime stemming from missing calls to device destructors at the end of programs. (#446)

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, David Ittah.

Catalyst v0.4.0

New features

• Catalyst is now accessible directly within the PennyLane user interface, once Catalyst is installed, allowing easy access to Catalyst just-in-time functionality.

  Through the use of the qml.qjit decorator, entire workflows can be JIT compiled down to a machine binary on first-function execution, including both quantum and classical processing. Subsequent calls to the compiled function will execute the previously-compiled binary, resulting in significant performance improvements.

  import pennylane as qml
  
  dev = qml.device("lightning.qubit", wires=2)
  
  @qml.qjit
  @qml.qnode(dev)
  def circuit(theta):
      qml.Hadamard(wires=0)
      qml.RX(theta, wires=1)
      qml.CNOT(wires=[0, 1])
      return qml.expval(qml.PauliZ(wires=1))

  >>> circuit(0.5)  # the first call, compilation occurs here
  array(0.)
  >>> circuit(0.5)  # the precompiled quantum function is called
  array(0.)

  Currently, PennyLane supports the Catalyst hybrid compiler with the qml.qjit decorator, which directly aliases Catalyst's catalyst.qjit.

  In addition to the above qml.qjit integration, the following native PennyLane functions can now be used with the qjit decorator: qml.adjoint, qml.ctrl, qml.grad, qml.jacobian, qml.vjp, qml.jvp, and qml.adjoint, qml.while_loop, qml.for_loop, qml.cond. These will alias to the corresponding Catalyst functions when used within a qjit context.

  For more details on these functions, please refer to the PennyLane compiler documentation and compiler module documentation.

• Just-in-time compiled functions now support asynchronous execution of QNodes. (#374) (#381) (#420) (#424) (#433)

  Simply specify async_qnodes=True when using the @qjit decorator to enable the async execution of QNodes. Currently, asynchronous execution is only supported by lightning.qubit and lightning.kokkos.

  Asynchronous execution will be most beneficial for just-in-time compiled functions that contain --- or generate --- multiple QNodes.

  For example,

  dev = qml.device("lightning.qubit", wires=2)
  
  @qml.qnode(device=dev)
  def circuit(params):
      qml.RX(params[0], wires=0)
      qml.RY(params[1], wires=1)
      qml.CNOT(wires=[0, 1])
      return qml.expval(qml.PauliZ(wires=0))
  
  @qjit(async_qnodes=True)
  def multiple_qnodes(params):
      x = jnp.sin(params)
      y = jnp.cos(params)
      z = jnp.array([circuit(x), circuit(y)]) # will be executed in parallel
      return circuit(z)

  >>> func(jnp.array([1.0, 2.0]))
  1.0
  

  Here, the first two circuit executions will occur in parallel across multiple threads, as their execution can occur independently.

• Preliminary support for PennyLane transforms has been added. (#280)

  @qjit
  @qml.transforms.split_non_commuting
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(x,wires=0)
      return [qml.expval(qml.PauliY(0)), qml.expval(qml.PauliZ(0))]

  >>> circuit(0.4)
  [array(-0.51413599), array(0.85770868)]

  Currently, most PennyLane transforms will work with Catalyst as long as:

  • The circuit does not include any Catalyst-specific features, such
    as Catalyst control flow or measurement,

  • The QNode returns only lists of measurement processes,

  • AutoGraph is disabled, and

  • The transformation does not require or depend on the numeric value of
    dynamic variables.

• Catalyst now supports just-in-time compilation of dynamically-shaped arrays. (#366) (#386) (#390) (#411)

  The @qjit decorator can now be used to compile functions that accepts or contain tensors whose dimensions are not known at compile time; runtime execution with different shapes is supported without recompilation.

  In addition, standard tensor initialization functions jax.numpy.ones, jnp.zeros, and jnp.empty now accept dynamic variables (where the value is only known at runtime).

  @qjit
  def func(size: int):
      return jax.numpy.ones([size, size], dtype=float)

  >>> func(3)
  [[1. 1. 1.]
   [1. 1. 1.]
   [1. 1. 1.]]

  When passing tensors as arguments to compiled functions, the abstracted_axes keyword argument to the @qjit decorator can be used to specify which axes of the input arguments should be treated as abstract (and thus avoid recompilation).

  For example, without specifying abstracted_axes, the following sum function would recompile each time an array of different size is passed as an argument:

  >>> @qjit
  >>> def sum_fn(x):
  >>>     return jnp.sum(x)
  >>> sum_fn(jnp.array([1]))     # Compilation happens here.
  >>> sum_fn(jnp.array([1, 1]))  # And here!

  By passing abstracted_axes, we can specify that the first axes of the first argument is to be treated as dynamic during initial compilation:

  >>> @qjit(abstracted_axes={0: "n"})
  >>> def sum_fn(x):
  >>>     return jnp.sum(x)
  >>> sum_fn(jnp.array([1]))     # Compilation happens here.
  >>> sum_fn(jnp.array([1, 1]))  # No need to recompile.

  Note that support for dynamic arrays in control-flow primitives (such as loops), is not yet supported.

• Error mitigation using the zero-noise extrapolation method is now available through the catalyst.mitigate_with_zne transform. (#324) (#414)

  For example, given a noisy device (such as noisy hardware available through Amazon Braket):

  dev = qml.device("noisy.device", wires=2)
  
  @qml.qnode(device=dev)
  def circuit(x, n):
  
      @for_loop(0, n, 1)
      def loop_rx(i):
          qml.RX(x, wires=0)
  
      loop_rx()
  
      qml.Hadamard(wires=0)
      qml.RZ(x, wires=0)
      loop_rx()
      qml.RZ(x, wires=0)
      qml.CNOT(wires=[1, 0])
      qml.Hadamard(wires=1)
      return qml.expval(qml.PauliY(wires=0))
  
  @qjit
  def mitigated_circuit(args, n):
      s = jax.numpy.array([1, 2, 3])
      return mitigate_with_zne(circuit, scale_factors=s)(args, n)

  >>> mitigated_circuit(0.2, 5)
  0.5655341100116512

  In addition, a mitigation dialect has been added to the MLIR layer of Catalyst. It contains a Zero Noise Extrapolation (ZNE) operation, with a lowering to a global folded circuit.

Improvements

• The three backend devices provided with Catalyst, lightning.qubit, lightning.kokkos, and braket.aws, are now dynamically loaded at runtime. (#343) (#400)

  This takes advantage of the new backend plugin system provided in Catalyst v0.3.2, and allows the devices to be packaged separately from the runtime CAPI. Provided backend devices are now loaded at runtime, instead of being linked at compile time.

  For more details on the backend plugin system, see the custom devices documentation.

• Finite-shot measurement statistics (expval, var, and probs) are now supported for the lightning.qubit and lightning.kokkos devices. Previously, exact statistics were returned even when finite shots were specified. (#392) (#410)

  >>> dev = qml.device("lightning.qubit", wires=2, shots=100)
  >>> @qjit
  >>> @qml.qnode(dev)
  >>> def circuit(x):
  >>>     qml.RX(x, wires=0)
  >>>     return qml.probs(wires=0)
  >>> circuit(0.54)
  array([0.94, 0.06])
  >>> circuit(0.54)
  array([0.93, 0.07])

• Catalyst gradient functions grad, jacobian, jvp, and vjp can now be invoked from outside a @qjit context. (#375)

  This simplifies the process of writing functions where compilation can be turned on and off easily by adding or removing the decorator. The functions dispatch to their JAX equivalents when the compilation is turned off.

  dev = qml.device("lightning.qubit", wires=2)
  
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(x, wires=0)
      return qml.expval(qml.PauliZ(0))

  >>> grad(circuit)(0.54)  # dispatches to jax.grad
  Array(-0.51413599, dtype=float64, weak_type=True)
  >>> qjit(grad(circuit))(0.54). # differentiates using Catalyst
  array(-0.51413599)

• New lightning.qubit configuration options are now supported via the qml.device loader, including Markov Chain Monte Carlo sampling support. (#369)

  dev = qml.device("lightning.qubit", wires=2, shots=1000, mcmc=True)
  
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(x, wires=0)
      return qml.expval(qml.PauliZ(0))

  >>> circuit(0.54)
  array(0.856)

• Improvements have been made to the runtime and quantum MLIR dialect in order to support asynchronous execution.

  • The runtime now su...

Catalyst v0.3.2-post1

This post-release updates the docs with up-to-date information & additional sections for the installation guide.

Catalyst v0.3.2

New features

• The experimental AutoGraph feature now supports Python while loops, allowing native Python loops to be captured and compiled with Catalyst. (#318)

  dev = qml.device("lightning.qubit", wires=4)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def circuit(n: int, x: float):
      i = 0
  
      while i < n:
          qml.RX(x, wires=i)
          i += 1
  
      return qml.expval(qml.PauliZ(0))

  >>> circuit(4, 0.32)
  array(0.94923542)

  This feature extends the existing AutoGraph support for Python for loops and if statements introduced in v0.3. Note that TensorFlow must be installed for AutoGraph support.

  For more details, please see the AutoGraph guide.

• In addition to loops and conditional branches, AutoGraph now supports native Python and, or and not operators in Boolean expressions. (#325)

  dev = qml.device("lightning.qubit", wires=1)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def circuit(x: float):
  
      if x >= 0 and x < jnp.pi:
          qml.RX(x, wires=0)
  
      return qml.probs()

  >>> circuit(0.43)
  array([0.95448287, 0.04551713])
  >>> circuit(4.54)
  array([1., 0.])

  Note that logical Boolean operators will only be captured by AutoGraph if all operands are dynamic variables (that is, a value known only at runtime, such as a measurement result or function argument). For other use cases, it is recommended to use the jax.numpy.logical_* set of functions where appropriate.

• Debug compiled programs and print dynamic values at runtime with debug.print (#279) (#356)

  You can now print arbitrary values from your running program, whether they are arrays, constants, strings, or abitrary Python objects. Note that while non-array Python objects will be printed at runtime, their string representation is captured at compile time, and thus will always be the same regardless of program inputs. The output for arrays optionally includes a descriptor for how the data is stored in memory ("memref").

  @qjit
  def func(x: float):
      debug.print(x, memref=True)
      debug.print("exit")

  >>> func(jnp.array(0.43))
  MemRef: base@ = 0x5629ff2b6680 rank = 0 offset = 0 sizes = [] strides = [] data =
  0.43
  exit

• Catalyst now officially supports macOS X86_64 devices, with macOS binary wheels available for both AARCH64 and X86_64. (#347) (#313)

• It is now possible to dynamically load third-party Catalyst compatible devices directly into a pre-installed Catalyst runtime on Linux. (#327)

  To take advantage of this, third-party devices must implement the Catalyst::Runtime::QuantumDevice interface, in addition to defining the following method:

  extern "C" Catalyst::Runtime::QuantumDevice*
  getCustomDevice() { return new CustomDevice(); }

  This support can also be integrated into existing PennyLane Python devices that inherit from the QuantumDevice class, by defining the get_c_interface static method.

  For more details, see the custom devices documentation.

Improvements

• Return values of conditional functions no longer need to be of exactly the same type. Type promotion is automatically applied to branch return values if their types don't match. (#333)

  @qjit
  def func(i: int, f: float):
  
      @cond(i < 3)
      def cond_fn():
          return i
  
      @cond_fn.otherwise
      def otherwise():
          return f
  
      return cond_fn()

  >>> func(1, 4.0)
  array(1.0)

  Automatic type promotion across conditional branches also works with AutoGraph:

  @qjit(autograph=True)
  def func(i: int, f: float):
  
      if i < 3:
          i = i
      else:
          i = f
  
      return i

  >>> func(1, 4.0)
  array(1.0)

• AutoGraph now supports converting functions even when they are invoked through functional wrappers such as adjoint, ctrl, grad, jacobian, etc. (#336)

  For example, the following should now succeed:

  def inner(n):
    for i in range(n):
      qml.T(i)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def f(n: int):
      adjoint(inner)(n)
      return qml.state()

• To prepare for Catalyst's frontend being integrated with PennyLane, the appropriate plugin entry point interface has been added to Catalyst. (#331)

  For any compiler packages seeking to be registered in PennyLane, the entry_points metadata under the the group name pennylane.compilers must be added, with the following try points:

  • context: Path to the compilation evaluation context manager. This context manager should have the method context.is_tracing(), which returns True if called within a program that is being traced or captured.

  • ops: Path to the compiler operations module. This operations module may contain compiler specific versions of PennyLane operations. Within a JIT context, PennyLane operations may dispatch to these.

  • qjit: Path to the JIT compiler decorator provided by the compiler. This decorator should have the signature qjit(fn, *args, **kwargs), where fn is the function to be compiled.

• The compiler driver diagnostic output has been improved, and now includes failing IR as well as the names of failing passes. (#349)

• The scatter operation in the Catalyst dialect now uses an SCF for loop to avoid ballooning the compiled code. (#307)

• The CopyGlobalMemRefPass pass of our MLIR processing pipeline now supports dynamically shaped arrays. (#348)

• The Catalyst utility dialect is now included in the Catalyst MLIR C-API. (#345)

• Fix an issue with the AutoGraph conversion system that would prevent the fallback to Python from working correctly in certain instances. (#352)

  The following type of code is now supported:

  @qjit(autograph=True)
  def f():
    l = jnp.array([1, 2])
    for _ in range(2):
        l = jnp.kron(l, l)
    return l

Breaking changes

• The axis ordering for catalyst.jacobian is updated to match jax.jacobian. Assuming we have parameters of shape [a,b] and results of shape [c,d], the returned Jacobian will now have shape [c, d, a, b] instead of [a, b, c, d]. (#283)

Bug fixes

• An upstream change in the PennyLane-Lightning project was addressed to prevent compilation issues in the StateVectorLQubitDynamic class in the runtime. The issue was introduced in #499. (#322)

• The requirements.txt file to build Catalyst from source has been updated with a minimum pip version, >=22.3. Previous versions of pip are unable to perform editable installs when the system-wide site-packages are read-only, even when the --user flag is provided. (#311)

• The frontend has been updated to make it compatible with PennyLane MeasurementProcess objects now being PyTrees in PennyLane version 0.33. (#315)

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi,
David Ittah,
Sergei Mironov,
Romain Moyard,
Erick Ochoa Lopez.

Catalyst v0.3.1-post1

This post-release updates the docs to include the AutoGraph guide.

Catalyst v0.3.1

New features

• The experimental AutoGraph feature, now supports Python for loops, allowing native Python loops to be captured and compiled with Catalyst. (#258)

  dev = qml.device("lightning.qubit", wires=n)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def f(n):
      for i in range(n):
          qml.Hadamard(wires=i)
  
      return qml.expval(qml.PauliZ(0))

  This feature extends the existing AutoGraph support for Python if statements introduced in v0.3. Note that TensorFlow must be installed for AutoGraph support.

• The quantum control operation can now be used in conjunction with Catalyst control flow, such as loops and conditionals, via the new catalyst.ctrl function. (#282)

  Similar in behaviour to the qml.ctrl control modifier from PennyLane, catalyst.ctrl can additionally wrap around quantum functions which contain control flow, such as the Catalyst cond, for_loop, and while_loop primitives.

  @qjit
  @qml.qnode(qml.device("lightning.qubit", wires=4))
  def circuit(x):
  
      @for_loop(0, 3, 1)
      def repeat_rx(i):
          qml.RX(x / 2, wires=i)
  
      catalyst.ctrl(repeat_rx, control=3)()
  
      return qml.expval(qml.PauliZ(0))

  >>> circuit(0.2)
  array(1.)

• Catalyst now supports JAX's array.at[index] notation for array element assignment and updating. (#273)

  @qjit
  def add_multiply(l: jax.core.ShapedArray((3,), dtype=float), idx: int):
      res = l.at[idx].multiply(3)
      res2 = l.at[idx].add(2)
      return res + res2
  
  res = add_multiply(jnp.array([0, 1, 2]), 2)

  >>> res
  [0, 2, 10]

  For more details on available methods, see the JAX documentation.

Improvements

• A new compiler driver has been implemented in C++. This improves compile-time performance by avoiding round-tripping, which is when the entire program being compiled is dumped to a textual form and re-parsed by another tool.

  This is also a requirement for providing custom metadata at the LLVM level, which is necessary for better integration with tools like Enzyme. Finally, this makes it more natural to improve error messages originating from C++ when compared to the prior subprocess-based approach. (#216)

• Support the braket.devices.Devices enum class and s3_destination_folder device options for AWS Braket remote devices. (#278)

• Improvements have been made to the build process, including avoiding unnecessary processes such as removing opt and downloading the wheel. (#298)

• Remove a linker warning about duplicate rpaths when Catalyst wheels are installed on macOS. (#314)

Bug fixes

• Fix incompatibilities with GCC on Linux introduced in v0.3.0 when compiling user programs. Due to these, Catalyst v0.3.0 only works when clang is installed in the user environment.

  • Resolve an issue with an empty linker flag, causing ld to error. (#276)

  • Resolve an issue with undefined symbols provided the Catalyst runtime. (#316)

• Remove undocumented package dependency on the zlib/zstd compression library. (#308)

• Fix filesystem issue when compiling multiple functions with the same name and keep_intermediate=True. (#306)

• Add support for applying the adjoint operation to QubitUnitary gates. QubitUnitary was not able to be adjointed when the variable holding the unitary matrix might change. This can happen, for instance, inside of a for loop. To solve this issue, the unitary matrix gets stored in the array list via push and pops. The unitary matrix is later reconstructed from the array list and QubitUnitary can be executed in the adjointed context. (#304) (#310)

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi,
David Ittah,
Erick Ochoa Lopez,
Jacob Mai Peng,
Sergei Mironov,
Romain Moyard.

Catalyst v0.3.0

New features

• Catalyst now officially supports macOS ARM devices, such as Apple M1/M2 machines, with macOS binary wheels available on PyPI. For more details on the changes involved to support macOS, please see the improvements section. (#229) (#232) (#233) (#234)

• Write Catalyst-compatible programs with native Python conditional statements. (#235)

  AutoGraph is a new, experimental, feature that automatically converts Python conditional statements like if, else, and elif, into their equivalent functional forms provided by Catalyst (such as catalyst.cond).

  This feature is currently opt-in, and requires setting the autograph=True flag in the qjit decorator:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qjit(autograph=True)
  @qml.qnode(dev)
  def f(x):
      if x < 0.5:
          qml.RY(jnp.sin(x), wires=0)
      else:
          qml.RX(jnp.cos(x), wires=0)
  
      return qml.expval(qml.PauliZ(0))

  The implementation is based on the AutoGraph module from TensorFlow, and requires a working TensorFlow installation be available. In addition, Python loops (for and while) are not yet supported, and do not work in AutoGraph mode.

  Note that there are some caveats when using this feature especially around the ues of global variables or object mutation inside of methods. A functional style is always recommended when using qjit or AutoGraph.

• The quantum adjoint operation can now be used in conjunction with Catalyst control flow, such as loops and conditionals. For this purpose a new instruction, catalyst.adjoint, has been added. (#220)

  catalyst.adjoint can wrap around quantum functions which contain the Catalyst cond, for_loop, and while_loop primitives. Previously, the usage of qml.adjoint on functions with these primitives would result in decomposition errors. Note that a future release of Catalyst will
  merge the behaviour of catalyst.adjoint into qml.adjoint for convenience.

  dev = qml.device("lightning.qubit", wires=3)
  
  @qjit
  @qml.qnode(dev)
  def circuit(x):
  
      @for_loop(0, 3, 1)
      def repeat_rx(i):
          qml.RX(x / 2, wires=i)
  
      adjoint(repeat_rx)()
  
      return qml.expval(qml.PauliZ(0))

  >>> circuit(0.2)
  array(0.99500417)

  Additionally, the ability to natively represent the adjoint construct in Catalyst's program representation (IR) was added.

• QJIT-compiled programs now support (nested) container types as inputs and outputs of compiled functions. This includes lists and dictionaries, as well as any data structure implementing the PyTree protocol. (#215) (#221)

  For example, a program that accepts and returns a mix of dictionaries, lists, and tuples:

  @qjit
  def workflow(params1, params2):
      res1 = params1["a"][0][0] + params2[1]
      return {"y1": jnp.sin(res1), "y2": jnp.cos(res1)}

  >>> params1 = {"a": [[0.1], 0.2]}
  >>> params2 = (0.6, 0.8)
  >>> workflow(params1, params2)
  array(0.78332691)

• Compile-time backpropagation of arbitrary hybrid programs is now supported, via integration with Enzyme AD. (#158) (#193) (#224) (#225) (#239) (#244)

  This allows catalyst.grad to differentiate hybrid functions that contain both classical pre-processing (inside & outside of QNodes), QNodes, as well as classical post-processing (outside of QNodes) via a combination of backpropagation and quantum gradient methods.

  The new default for the differentiation method attribute in catalyst.grad has been changed to "auto", which performs Enzyme-based reverse mode AD on classical code, in conjunction with the quantum diff_method specified on each QNode:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qml.qnode(dev, diff_method="parameter-shift")
  def circuit(theta):
      qml.RX(jnp.exp(theta ** 2) / jnp.cos(theta / 4), wires=0)
      return qml.expval(qml.PauliZ(wires=0))

  >>> grad = qjit(catalyst.grad(circuit, method="auto"))
  >>> grad(jnp.pi)
  array(0.05938718)

  The reworked differentiation pipeline means you can now compute exact derivatives of programs with both classical pre- and post-processing, as shown below:

  @qml.qnode(qml.device("lightning.qubit", wires=1), diff_method="adjoint")
  def circuit(theta):
      qml.RX(jnp.exp(theta ** 2) / jnp.cos(theta / 4), wires=0)
      return qml.expval(qml.PauliZ(wires=0))
  
  def loss(theta):
      return jnp.pi / jnp.tanh(circuit(theta))
  
  @qjit
  def grad_loss(theta):
      return catalyst.grad(loss)(theta)

  >>> grad_loss(1.0)
  array(-1.90958669)

  You can also use multiple QNodes with different differentiation methods:

  @qml.qnode(qml.device("lightning.qubit", wires=1), diff_method="parameter-shift")
  def circuit_A(params):
      qml.RX(jnp.exp(params[0] ** 2) / jnp.cos(params[1] / 4), wires=0)
      return qml.probs()
  
  @qml.qnode(qml.device("lightning.qubit", wires=1), diff_method="adjoint")
  def circuit_B(params):
      qml.RX(jnp.exp(params[1] ** 2) / jnp.cos(params[0] / 4), wires=0)
      return qml.expval(qml.PauliZ(wires=0))
  
  def loss(params):
      return jnp.prod(circuit_A(params)) + circuit_B(params)
  
  @qjit
  def grad_loss(theta):
      return catalyst.grad(loss)(theta)

  >>> grad_loss(jnp.array([1.0, 2.0]))
  array([ 0.57367285, 44.4911605 ])

  And you can differentiate purely classical functions as well:

  def square(x: float):
      return x ** 2
  
  @qjit
  def dsquare(x: float):
      return catalyst.grad(square)(x)

  >>> dsquare(2.3)
  array(4.6)

  Note that the current implementation of reverse mode AD is restricted to 1st order derivatives, but you can still use catalyst.grad(method="fd") is still available to perform a finite differences approximation of any differentiable function.

• Add support for the new PennyLane arithmetic operators. (#250)

  PennyLane is in the process of replacing Hamiltonian and Tensor observables with a set of general arithmetic operators. These consist of Prod, Sum and SProd.

  By default, using dunder methods (eg. +, -, @, *) to combine operators with scalars or other operators will create Hamiltonian and Tensor objects. However, these two methods will be deprecated in coming releases of PennyLane.

  To enable the new arithmetic operators, one can use Prod, Sum, and Sprod directly or activate them by calling enable_new_opmath at the beginning of your PennyLane program.

  dev = qml.device("lightning.qubit", wires=2)
  
  @qjit
  @qml.qnode(dev)
  def circuit(x: float, y: float):
      qml.RX(x, wires=0)
      qml.RX(y, wires=1)
      qml.CNOT(wires=[0, 1])
      return qml.expval(0.2 * qml.PauliX(wires=0) - 0.4 * qml.PauliY(wires=1))

  >>> qml.operation.enable_new_opmath()
  >>> qml.operation.active_new_opmath()
  True
  >>> circuit(np.pi / 4, np.pi / 2)
  array(0.28284271)

Improvements

• Better support for Hamiltonian observables:

  • Allow Hamiltonian observables with integer coefficients. (#248)

    For example, compiling the following circuit wasn't previously allowed, but is now supported in Catalyst:

    dev = qml.device("lightning.qubit", wires=2)
    
    @qjit
    @qml.qnode(dev)
    def circuit(x: float, y: float):
        qml.RX(x, wires=0)
        qml.RY(y, wires=1)
    
        coeffs = [1, 2]
        obs = [qml.PauliZ(0), qml.PauliZ(1)]
        return qml.expval(qml.Hamiltonian(coeffs, obs))

  • Allow nested Hamiltonian observables. (#255)

    @qjit
    @qml.qnode(qml.device("lightning.qubit", wires=3))
    def circuit(x, y, coeffs1, coeffs2):
        qml.RX(x, wires=0)
        qml.RX(y, wires=1)
        qml.RY(x + y, wires=2)
    
        obs = [
            qml.PauliX(0) @ qml.PauliZ(1),
            qml.Hamiltonian(coeffs1, [qml.PauliZ(0) @ qml.Hadamard(2)]),
        ]
    
        return qml.var(qml.Hamiltonian(coeffs2, obs))

• Various performance improvements:

  • The execution and compile time of programs has been reduced, by generating more efficient code and avoiding unnecessary optimizations. Specifically, a scalarization procedure was added to the MLIR pass pipeline, and LLVM IR compilation is now invoked with optimization level 0. (#217)

  • The execution time of compile...

Catalyst v0.2.1

Bug fixes

• Add missing OpenQASM backend in binary distribution, which relies on the latest version of the AWS Braket plugin for PennyLane to resolve dependency issues between the plugin, Catalyst, and PennyLane. The Lightning-Kokkos backend with Serial and OpenMP modes is also added to the binary distribution. #198

Improvements

• When using OpenQASM-based devices the string representation of the circuit is printed on exception. #199

• Use pybind11::module interface library instead of pybind11::embed in the runtime for OpenQasm backend to avoid linking to the python library at compile time. #200

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, David Ittah.