Release notes

This page contains the release notes for Catalyst.

Release 0.6.0

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 observables. (#648)

  • The endianness of counts in Catalyst now matches the convention of PennyLane. (#601)

  • catalyst.debug.print no longer supports the memref keyword argument. Please use catalyst.debug.print_memref instead. (#621)

Bug fixes

  • The QNode argument diff_method=None is now supported for QNodes within a qjit-compiled function. (#658)

  • A bug has been fixed where the C++ compiler driver was incorrectly being triggered twice. (#594)

  • Programs with jnp.reshape no longer fail. (#592)

  • A bug in the quantum adjoint routine in the compiler has been fixed, which didn’t take into account control wires on operations in all instances. (#591)

  • A bug in the test suite causing stochastic autograph test failures has been fixed. (#652)

  • Running Catalyst tests should no longer raise ResourceWarning from the use of tempfile.TemporaryDirectory. (#676)

  • Raises an exception if the user has an incompatible CUDA Quantum version installed. (#707)

Internal changes

  • The deprecated @qfunc decorator, in use mainly by the LIT test suite, has been removed. (#679)

  • Catalyst now publishes a revision string under catalyst.__revision__, in addition to the existing catalyst.__version__ string. The revision contains the Git commit hash of the repository at the time of packaging, or for editable installations the active commit hash at the time of package import. (#560)

  • The Python interpreter is now a shared resource across the runtime. (#615)

    This change allows any part of the runtime to start executing Python code through pybind.

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, David Ittah, Romain Moyard, Sergei Mironov, Erick Ochoa Lopez, Lee James O’Riordan, Muzammiluddin Syed.

Release 0.5.0

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++ state-vector 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()
    
  • Add support for GlobalPhase gate in the runtime. (#563)

    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 with the provided arguments.

    • print_compilation_stage(fn, stage) to print one of the recorded compilation stages for a JIT-compiled function.

    For more details, please see the catalyst.debug documentation.

  • Remove redundant copies of TOML files for lightning.kokkos and lightning.qubit. (#472)

    lightning.kokkos and lightning.qubit now ship with their own TOML file. As such, we use the TOML file provided by them.

  • Capturing quantum circuits with many gates prior to compilation is now quadratically faster (up to a factor), by removing qextract_p and qinst_p from forced-order primitives. (#469)

  • Update AllocateQubit and AllocateQubits in LightningKokkosSimulator to preserve the current state-vector before qubit re-allocations in the runtime dynamic qubits management. (#479)

  • The PennyLane custom compiler entry point name convention has changed, necessitating a change to the Catalyst entry points. (#493)

Breaking changes

  • Catalyst gradient functions now match the Jax convention for the returned axes of gradients, Jacobians, VJPs, and JVPs. As a result, the returned tensor shape from various Catalyst gradient functions may differ compared to previous versions of Catalyst. (#500) (#501) (#508)

  • The Catalyst Python frontend has been partially refactored. The impact on user-facing functionality is minimal, but the location of certain classes and methods used by the package may have changed. (#529) (#522)

    The following changes have been made:

    • Some debug methods and features on the QJIT class have been turned into free functions and moved to the catalyst.debug module, which will now appear in the public documention. This includes compiling a program from IR, obtaining a C program to invoke a compiled function from, and printing fine-grained MLIR compilation stages.

    • The compilation_pipelines.py module has been renamed to jit.py, and certain functionality has been moved out (see following items).

    • A new module compiled_functions.py now manages low-level access to compiled functions.

    • A new module tracing/type_signatures.py handles functionality related managing arguments and type signatures during the tracing process.

    • The contexts.py module has been moved from utils to the new tracing sub-module.

Internal changes

  • Changes to the runtime QIR API and dependencies, to avoid symbol conflicts with other libraries that utilize QIR. (#464) (#470)

    The existing Catalyst runtime implements QIR as a library that can be linked against a QIR module. This works great when Catalyst is the only implementor of QIR, however it may generate symbol conflicts when used alongside other QIR implementations.

    To avoid this, two changes were necessary:

    • The Catalyst runtime now has a different API from QIR instructions.

      The runtime has been modified such that QIR instructions are lowered to functions where the __quantum__ part of the function name is replaced with __catalyst__. This prevents the possibility of symbol conflicts with other libraries that implement QIR as a library.

    • The Catalyst runtime no longer depends on QIR runner’s stdlib.

      We no longer depend nor link against QIR runner’s stdlib. By linking against QIR runner’s stdlib, some definitions persisted that may be different than ones used by third party implementors. To prevent symbol conflicts QIR runner’s stdlib was removed and is no longer linked against. As a result, the following functions are now defined and implemented in Catalyst’s runtime:

      • int64_t __catalyst__rt__array_get_size_1d(QirArray *)

      • int8_t *__catalyst__rt__array_get_element_ptr_1d(QirArray *, int64_t)

      and the following functions were removed since the frontend does not generate them

      • QirString *__catalyst__rt__qubit_to_string(QUBIT *)

      • QirString *__catalyst__rt__result_to_string(RESULT *)

  • Fix an issue when no qubit number was specified for the qinst primitive. The primitive now correctly deduces the number of qubits when no gate parameters are present. This change is not user facing. (#496)

Bug fixes

  • Fixed a bug where differentiation of sliced arrays would result in an error. (#552)

    def f(x):
      return jax.numpy.sum(x[::2])
    
    x = jax.numpy.array([0.1, 0.2, 0.3, 0.4])
    
    >>> catalyst.qjit(catalyst.grad(f))(x)
    [1. 0. 1. 0.]
    
  • Fixed a bug where quantum control applied to a subcircuit was not correctly mapping wires, and the wires in the nested region remained unchanged. (#555)

  • Catalyst will no longer print a warning that recompilation is triggered when a @qjit decorated function with no arguments is invoke without having been compiled first, for example via the use of target="mlir". (#522)

  • Fixes a bug in the configuration of dynamic shaped arrays that would cause certain program to error with TypeError: cannot unpack non-iterable ShapedArray object. (#526)

    This is fixed by replacing the code which updates the JAX_DYNAMIC_SHAPES option with a transient_jax_config() context manager which temporarily sets the value of JAX_DYNAMIC_SHAPES to True and then restores the original configuration value following the yield. The context manager is used by trace_to_jaxpr() and lower_jaxpr_to_mlir().

  • Exceptions encountered in the runtime when using the @qjit option async_qnodes=Tue will now be properly propagated to the frontend. (#447) (#510)

    This is done by:

    • changeing llvm.call to llvm.invoke

    • setting async runtime tokens and values to be errors

    • deallocating live tokens and values

  • Fixes a bug when computing gradients with the indexing/slicing, by fixing the scatter operation lowering when updatedWindowsDim is empty. (#475)

  • Fix the issue in LightningKokkos::AllocateQubits with allocating too many qubit IDs on qubit re-allocation. (#473)

  • Fixed an issue where wires was incorrectly set as <Wires = [<WiresEnum.AnyWires: -1>]> when using catalyst.adjoint and catalyst.ctrl, by adding a wires property to these operations. (#480)

  • Fix the issue with multiple lapack symbol definitions in the compiled program by updating the stablehlo.custom_call conversion pass. (#488)

Contributors

This release contains contributions from (in alphabetical order):

Mikhail Andrenkov, Ali Asadi, David Ittah, Tzung-Han Juang, Erick Ochoa Lopez, Romain Moyard, Raul Torres, Haochen Paul Wang.

Release 0.4.1

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.

Release 0.4.0

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 asynchronuous 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 indepdently.

  • 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 supports multiple active devices managed via a device pool. The new RTDevice data-class and RTDeviceStatus along with the thread_local device instance pointer enable the runtime to better scope the lifetime of device instances concurrently. With these changes, one can create multiple active devices and execute multiple programs in a multithreaded environment. (#381)

    • The ability to dynamically release devices has been added via DeviceReleaseOp in the Quantum MLIR dialect. This is lowered to the __quantum__rt__device_release() runtime instruction, which updates the status of the device instance from Active to Inactive. The runtime will reuse this deactivated instance instead of creating a new one automatically at runtime in a multi-QNode workflow when another device with identical specifications is requested. (#381)

    • The DeviceOp definition in the Quantum MLIR dialect has been updated to lower a tuple of device information ('lib', 'name', 'kwargs') to a single device initialization call __quantum__rt__device_init(int8_t *, int8_t *, int8_t *). This allows the runtime to initialize device instances without keeping partial information of the device (#396)

  • The quantum adjoint compiler routine has been extended to support function calls that affect the quantum state within an adjoint region. Note that the function may only provide a single result consisting of the quantum register. By itself this provides no user-facing changes, but compiler pass developers may now generate quantum adjoint operations around a block of code containing function calls as well as quantum operations and control flow operations. (#353)

  • The allocation and deallocation operations in MLIR (AllocOp, DeallocOp) now follow simple value semantics for qubit register values, instead of modelling memory in the MLIR trait system. Similarly, the frontend generates proper value semantics by deallocating the final register value.

    The change enables functions at the MLIR level to accept and return quantum register values, which would otherwise not be correctly identified as aliases of existing register values by the bufferization system. (#360)

Breaking changes

  • Third party devices must now provide a configuration TOML file, in order to specify their supported operations, measurements, and features for Catalyst compatibility. For more information please visit the Custom Devices section in our documentation. (#369)

Bug fixes

  • Resolves a bug in the compiler’s differentiation engine that results in a segmentation fault when attempting to differentiate non-differentiable quantum operations. The fix ensures that all existing quantum operation types are removed during gradient passes that extract classical code from a QNode function. It also adds a verification step that will raise an error if a gradient pass cannot successfully eliminate all quantum operations for such functions. (#397)

  • Resolves a bug that caused unpredictable behaviour when printing string values with the debug.print function. The issue was caused by non-null-terminated strings. (#418)

Contributors

This release contains contributions from (in alphabetical order):

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

Release 0.3.2

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 entry 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
    
  • Catalyst now supports jax.numpy.polyfit inside a qjitted function. (#367)

  • Catalyst now supports custom calls (including the one from HLO). We added support in MLIR (operation, bufferization and lowering). In the lib_custom_calls, developers then implement their custom calls and use external functions directly (e.g. Lapack). The OpenBlas library is taken from Scipy and linked in Catalyst, therefore any function from it can be used. (#367)

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.

Release 0.3.1

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

  • The Lightning backend device has been updated to work with the new PL-Lightning monorepo. (#259) (#277)

  • 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.

Release 0.3.0

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 use 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 compiled functions has been improved in the frontend. (#213)

      Specifically, the following changes have been made, which leads to a small but measurable improvement when using larger matrices as inputs, or functions with many inputs:

      • only loading the user program library once per compilation,

      • generating return value types only once per compilation,

      • avoiding unnecessary type promotion, and

      • avoiding unnecessary array copies.

    • Peak memory utilization of a JIT compiled program has been reduced, by allowing tensors to be scheduled for deallocation. Previously, the tensors were not deallocated until the end of the call to the JIT compiled function. (#201)

  • Various improvements have been made to enable Catalyst to compile on macOS:

    • Remove unnecessary reinterpret_cast from ObsManager. Removal of these reinterpret_cast allows compilation of the runtime to succeed in macOS. macOS uses an ILP32 mode for Aarch64 where they use the full 64 bit mode but with 32 bit Integer, Long, and Pointers. This patch also changes a test file to prevent a mismatch in machines which compile using ILP32 mode. (#229)

    • Allow runtime to be compiled on macOS. Substitute nproc with a call to os.cpu_count() and use correct flags for ld.64. (#232)

    • Improve portability on the frontend to be available on macOS. Use .dylib, remove unnecessary flags, and address behaviour difference in flags. (#233)

    • Small compatibility changes in order for all integration tests to succeed on macOS. (#234)

  • Dialects can compile with older versions of clang by avoiding type mismatches. (#228)

  • The runtime is now built against qir-stdlib pre-build artifacts. (#236)

  • Small improvements have been made to the CI/CD, including fixing the Enzyme cache, generalize caches to other operating systems, fix build wheel recipe, and remove references to QIR in runtime’s Makefile. (#243) (#247)

Breaking changes

  • Support for Python 3.8 has been removed. (#231)

  • The default differentiation method on grad and jacobian is reverse-mode automatic differentiation instead of finite differences. When a QNode does not have a diff_method specified, it will default to using the parameter shift method instead of finite-differences. (#244) (#271)

  • The JAX version used by Catalyst has been updated to v0.4.14, the minimum PennyLane version required is now v0.32. (#264)

  • Due to the change allowing Python container objects as inputs to QJIT-compiled functions, Python lists are no longer automatically converted to JAX arrays. (#231)

    This means that indexing on lists when the index is not static will cause a TracerIntegerConversionError, consistent with JAX’s behaviour.

    That is, the following example is no longer support:

    @qjit
    def f(x: list, index: int):
        return x[index]
    

    However, if the parameter x above is a JAX or NumPy array, the compilation will continue to succeed.

  • The catalyst.grad function has been renamed to catalyst.jacobian and supports differentiation of functions that return multiple or non-scalar outputs. A new catalyst.grad function has been added that enforces that it is differentiating a function with a single scalar return value. (#254)

Bug fixes

  • Fixed an issue preventing the differentiation of qml.probs with the parameter-shift method. (#211)

  • Fixed the incorrect return value data-type with functions returning qml.counts. (#221)

  • Fix segmentation fault when differentiating a function where a quantum measurement is used multiple times by the same operation. (#242)

Contributors

This release contains contributions from (in alphabetical order):

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

Release 0.2.1

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

  • Return a list of decompositions when calling the decomposition method for control operations. This allows Catalyst to be compatible with upstream PennyLane. #241

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.

Release 0.2.0

New features

  • Catalyst programs can now be used inside of a larger JAX workflow which uses JIT compilation, automatic differentiation, and other JAX transforms. #96 #123 #167 #192

    For example, call a Catalyst qjit-compiled function from within a JAX jit-compiled function:

    dev = qml.device("lightning.qubit", wires=1)
    
    @qjit
    @qml.qnode(dev)
    def circuit(x):
        qml.RX(jnp.pi * x[0], wires=0)
        qml.RY(x[1] ** 2, wires=0)
        qml.RX(x[1] * x[2], wires=0)
        return qml.probs(wires=0)
    
    @jax.jit
    def cost_fn(weights):
        x = jnp.sin(weights)
        return jnp.sum(jnp.cos(circuit(x)) ** 2)
    
    >>> cost_fn(jnp.array([0.1, 0.2, 0.3]))
    Array(1.32269195, dtype=float64)
    

    Catalyst-compiled functions can now also be automatically differentiated via JAX, both in forward and reverse mode to first-order,

    >>> jax.grad(cost_fn)(jnp.array([0.1, 0.2, 0.3]))
    Array([0.49249037, 0.05197949, 0.02991883], dtype=float64)
    

    as well as vectorized using jax.vmap:

    >>> jax.vmap(cost_fn)(jnp.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]))
    Array([1.32269195, 1.53905377], dtype=float64)
    

    In particular, this allows for a reduction in boilerplate when using JAX-compatible optimizers such as jaxopt:

    >>> opt = jaxopt.GradientDescent(cost_fn)
    >>> params = jnp.array([0.1, 0.2, 0.3])
    >>> (final_params, _) = jax.jit(opt.run)(params)
    >>> final_params
    Array([-0.00320799,  0.03475223,  0.29362844], dtype=float64)
    

    Note that, in general, best performance will be seen when the Catalyst @qjit decorator is used to JIT the entire hybrid workflow. However, there may be cases where you may want to delegate only the quantum part of your workflow to Catalyst, and let JAX handle classical components (for example, due to missing a feature or compatibility issue in Catalyst).

  • Support for Amazon Braket devices provided via the PennyLane-Braket plugin. #118 #139 #179 #180

    This enables quantum subprograms within a JIT-compiled Catalyst workflow to execute on Braket simulator and hardware devices, including remote cloud-based simulators such as SV1.

    def circuit(x, y):
        qml.RX(y * x, wires=0)
        qml.RX(x * 2, wires=1)
        return qml.expval(qml.PauliY(0) @ qml.PauliZ(1))
    
    @qjit
    def workflow(x: float, y: float):
        device = qml.device("braket.local.qubit", backend="braket_sv", wires=2)
        g = qml.qnode(device)(circuit)
        h = catalyst.grad(g)
        return h(x, y)
    
    workflow(1.0, 2.0)
    

    For a list of available devices, please see the PennyLane-Braket documentation.

    Internally, the quantum instructions are generating OpenQASM3 kernels at runtime; these are then executed on both local (braket.local.qubit) and remote (braket.aws.qubit) devices backed by Amazon Braket Python SDK,

    with measurement results then propagated back to the frontend.

    Note that at initial release, not all Catalyst features are supported with Braket. In particular, dynamic circuit features, such as mid-circuit measurements, will not work with Braket devices.

  • Catalyst conditional functions defined via @catalyst.cond now support an arbitrary number of ‘else if’ chains. #104

    dev = qml.device("lightning.qubit", wires=1)
    
    @qjit
    @qml.qnode(dev)
    def circuit(x):
    
        @catalyst.cond(x > 2.7)
        def cond_fn():
            qml.RX(x, wires=0)
    
        @cond_fn.else_if(x > 1.4)
        def cond_elif():
            qml.RY(x, wires=0)
    
        @cond_fn.otherwise
        def cond_else():
            qml.RX(x ** 2, wires=0)
    
        cond_fn()
    
        return qml.probs(wires=0)
    
  • Iterating in reverse is now supported with constant negative step sizes via catalyst.for_loop. #129

    dev = qml.device("lightning.qubit", wires=1)
    
    @qjit
    @qml.qnode(dev)
    def circuit(n):
    
        @catalyst.for_loop(n, 0, -1)
        def loop_fn(_):
            qml.PauliX(0)
    
        loop_fn()
        return measure(0)
    
  • Additional gradient transforms for computing the vector-Jacobian product (VJP) and Jacobian-vector product (JVP) are now available in Catalyst. #98

    Use catalyst.vjp to compute the forward-pass value and VJP:

    @qjit
    def vjp(params, cotangent):
        def f(x):
            y = [jnp.sin(x[0]), x[1] ** 2, x[0] * x[1]]
            return jnp.stack(y)
    
        return catalyst.vjp(f, [params], [cotangent])
    
    >>> x = jnp.array([0.1, 0.2])
    >>> dy = jnp.array([-0.5, 0.1, 0.3])
    >>> vjp(x, dy)
    [array([0.09983342, 0.04      , 0.02      ]),
     array([-0.43750208,  0.07000001])]
    

    Use catalyst.jvp to compute the forward-pass value and JVP:

    @qjit
    def jvp(params, tangent):
        def f(x):
            y = [jnp.sin(x[0]), x[1] ** 2, x[0] * x[1]]
            return jnp.stack(y)
    
        return catalyst.jvp(f, [params], [tangent])
    
    >>> x = jnp.array([0.1, 0.2])
    >>> tangent = jnp.array([0.3, 0.6])
    >>> jvp(x, tangent)
    [array([0.09983342, 0.04      , 0.02      ]),
     array([0.29850125, 0.24000006, 0.12      ])]
    
  • Support for multiple backend devices within a single qjit-compiled function is now available. #86 #89

    For example, if you compile the Catalyst runtime with lightning.kokkos support (via the compilation flag ENABLE_LIGHTNING_KOKKOS=ON), you can use lightning.qubit and lightning.kokkos within a singular workflow:

    dev1 = qml.device("lightning.qubit", wires=1)
    dev2 = qml.device("lightning.kokkos", wires=1)
    
    @qml.qnode(dev1)
    def circuit1(x):
        qml.RX(jnp.pi * x[0], wires=0)
        qml.RY(x[1] ** 2, wires=0)
        qml.RX(x[1] * x[2], wires=0)
        return qml.var(qml.PauliZ(0))
    
    @qml.qnode(dev2)
    def circuit2(x):
    
        @catalyst.cond(x > 2.7)
        def cond_fn():
            qml.RX(x, wires=0)
    
        @cond_fn.otherwise
        def cond_else():
            qml.RX(x ** 2, wires=0)
    
        cond_fn()
    
        return qml.probs(wires=0)
    
    @qjit
    def cost(x):
        return circuit2(circuit1(x))
    
    >>> x = jnp.array([0.54, 0.31])
    >>> cost(x)
    array([0.80842369, 0.19157631])
    
  • Support for returning the variance of Hamiltonians, Hermitian matrices, and Tensors via qml.var has been added. #124

    dev = qml.device("lightning.qubit", wires=2)
    
    @qjit
    @qml.qnode(dev)
    def circuit(x):
        qml.RX(jnp.pi * x[0], wires=0)
        qml.RY(x[1] ** 2, wires=1)
        qml.CNOT(wires=[0, 1])
        qml.RX(x[1] * x[2], wires=0)
        return qml.var(qml.PauliZ(0) @ qml.PauliX(1))
    
    >>> x = jnp.array([0.54, 0.31])
    >>> circuit(x)
    array(0.98851544)
    

Breaking changes

  • The catalyst.grad function now supports using the differentiation method defined on the QNode (via the diff_method argument) rather than applying a global differentiation method. #163

    As part of this change, the method argument now accepts the following options:

    • method="auto": Quantum components of the hybrid function are differentiated according to the corresponding QNode diff_method, while the classical computation is differentiated using traditional auto-diff.

      With this strategy, Catalyst only currently supports QNodes with diff_method="param-shift" anddiff_method=”adjoint”`.

    • method="fd": First-order finite-differences for the entire hybrid function. The diff_method argument for each QNode is ignored.

    This is an intermediate step towards differentiating functions that internally call multiple QNodes, and towards supporting differentiation of classical postprocessing.

Improvements

  • Catalyst has been upgraded to work with JAX v0.4.13. #143 #185

  • Add a Backprop operation for using autodifferentiation (AD) at the LLVM level with Enzyme AD. The Backprop operations has a bufferization pattern and a lowering to LLVM. #107 #116

  • Error handling has been improved. The runtime now throws more descriptive and unified expressions for runtime errors and assertions. #92

  • In preparation for easier debugging, the compiler has been refactored to allow easy prototyping of new compilation pipelines. #38

    In the future, this will allow the ability to generate MLIR or LLVM-IR by loading input from a string or file, rather than generating it from Python.

    As part of this refactor, the following changes were made:

    • Passes are now classes. This allows developers/users looking to change flags to inherit from these passes and change the flags.

    • Passes are now passed as arguments to the compiler. Custom passes can just be passed to the compiler as an argument, as long as they implement a run method which takes an input and the output of this method can be fed to the next pass.

  • Improved Python compatibility by providing a stable signature for user generated functions. #106

  • Handle C++ exceptions without unwinding the whole stack. #99

  • Reduce the number of classical invocations by counting the number of gate parameters in the argmap function. #136

    Prior to this, the computation of hybrid gradients executed all of the classical code being differentiated in a pcount function that solely counted the number of gate parameters in the quantum circuit. This was so argmap and other downstream functions could allocate memrefs large enough to store all gate parameters.

    Now, instead of counting the number of parameters separately, a dynamically-resizable array is used in the argmap function directly to store the gate parameters. This removes one invocation of all of the classical code being differentiated.

  • Use Tablegen to define MLIR passes instead of C++ to reduce overhead of adding new passes. #157

  • Perform constant folding on wire indices for quantum.insert and quantum.extract ops, used when writing (resp. reading) qubits to (resp. from) quantum registers. #161

  • Represent known named observables as members of an MLIR Enum rather than a raw integer. This improves IR readability. #165

Bug fixes

  • Fix a bug in the mapping from logical to concrete qubits for mid-circuit measurements. #80

  • Fix a bug in the way gradient result type is inferred. #84

  • Fix a memory regression and reduce memory footprint by removing unnecessary temporary buffers. #100

  • Provide a new abstraction to the QuantumDevice interface in the runtime called DataView. C++ implementations of the interface can iterate through and directly store results into the DataView independent of the underlying memory layout. This can eliminate redundant buffer copies at the interface boundaries, which has been applied to existing devices. #109

  • Reduce memory utilization by transferring ownership of buffers from the runtime to Python instead of copying them. This includes adding a compiler pass that copies global buffers into the heap as global buffers cannot be transferred to Python. #112

  • Temporary fix of use-after-free and dependency of uninitialized memory. #121

  • Fix file renaming within pass pipelines. #126

  • Fix the issue with the do_queue deprecation warnings in PennyLane. #146

  • Fix the issue with gradients failing to work with hybrid functions that contain constant jnp.array objects. This will enable PennyLane operators that have data in the form of a jnp.array, such as a Hamiltonian, to be included in a qjit-compiled function. #152

    An example of a newly supported workflow:

    coeffs = jnp.array([0.1, 0.2])
    terms = [qml.PauliX(0) @ qml.PauliZ(1), qml.PauliZ(0)]
    H = qml.Hamiltonian(coeffs, terms)
    
    @qjit
    @qml.qnode(qml.device("lightning.qubit", wires=2))
    def circuit(x):
      qml.RX(x[0], wires=0)
      qml.RY(x[1], wires=0)
      qml.CNOT(wires=[0, 1])
      return qml.expval(H)
    
    params = jnp.array([0.3, 0.4])
    jax.grad(circuit)(params)
    

Contributors

This release contains contributions from (in alphabetical order):

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

Release 0.1.2

New features

  • Add an option to print verbose messages explaining the compilation process. #68

  • Allow catalyst.grad to be used on any traceable function (within a qjit context). This means the operation is no longer restricted to acting on qml.qnodes only. #75

Improvements

  • Work in progress on a Lightning-Kokkos backend:

    Bring feature parity to the Lightning-Kokkos backend simulator. #55

    Add support for variance measurements for all observables. #70

  • Build the runtime against qir-stdlib v0.1.0. #58

  • Replace input-checking assertions with exceptions. #67

  • Perform function inlining to improve optimizations and memory management within the compiler. #72

Breaking changes

Bug fixes

  • Several fixes to address memory leaks in the compiled program:

    Fix memory leaks from data that flows back into the Python environment. #54

    Fix memory leaks resulting from partial bufferization at the MLIR level. This fix makes the necessary changes to reintroduce the -buffer-deallocation pass into the MLIR pass pipeline. The pass guarantees that all allocations contained within a function (that is allocations that are not returned from a function) are also deallocated. #61

    Lift heap allocations for quantum op results from the runtime into the MLIR compiler core. This allows all memref buffers to be memory managed in MLIR using the MLIR bufferization infrastructure. #63

    Eliminate all memory leaks by tracking memory allocations at runtime. The memory allocations which are still alive when the compiled function terminates, will be freed in the finalization / teardown function. #78

  • Fix returning complex scalars from the compiled function. #77

Contributors

This release contains contributions from (in alphabetical order):

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

Release 0.1.1

New features

  • Adds support for interpreting control flow operations. #31

Improvements

  • Adds fallback compiler drivers to increase reliability during linking phase. Also adds support for a CATALYST_CC environment variable for manual specification of the compiler driver used for linking. #30

Breaking changes

Bug fixes

  • Fixes the Catalyst image path in the readme to properly render on PyPI.

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, Erick Ochoa Lopez.

Release 0.1.0

Initial public release.

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, Sam Banning, David Ittah, Josh Izaac, Erick Ochoa Lopez, Sergei Mironov, Isidor Schoch.