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Release notes

This page contains the release notes for Catalyst.

Release 0.2.0-dev

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

    Note that generally Catalyst should be used to JIT the entire workflow, but sometimes users may wish to delegate only the quantum part of their workflow to Catalyst and let JAX handle the rest (for example due to missing a feature or compatibility issue in Catalyst).

    Examples of newly supported workflows:

    • JIT compilation with JAX:

      @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]))

    • Automatic differentiation with JAX, both in forward and reverse mode, but to first-order only:

      @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)

def cost_fn(weights):
    x = jnp.sin(weights)
    return jnp.sum(jnp.cos(circuit(x)) ** 2)

jax.grad(cost_fn)(jnp.array([0.1, 0.2, 0.3]))

  • Add a Backprop operation with Bufferiation #107

  • Add support for else if chains for @cond conditionals #104

  • Add the end-to-end support for multiple backend devices. The compilation flag ENABLE_LIGHTNING_KOKKOS=ON builds the runtime with support for PennyLane’s lightning.kokkos. Both lightning.qubit and lightning.kokkos can be chosen as available backend devices from the frontend. #89

  • Add support for var of general observables #124

  • Add support for Jacobian product operations. The function jvp(f, params, tangents) returns the Jacobian-vector product of a function f, and the vjp(f, params, cotangents) returns its vector-Jacobian product.

    #98

    An example of a newly supported workflow:

from catalyst import jvp

def f(p):
  return jnp.stack([1*p, 2*p, 3*p])

@qjit
def workflow(params, tangent):
    return jvp(f, [params], [tangent])

workflow(jnp.zeros([4], dtype=float), jnp.ones([4], dtype=float))

Improvements

  • Improving error handling by throwing descriptive and unified expressions for runtime errors and assertions. #92

  • Improve interface for adding and re-using flags to quantum-opt commands. These are called pipelines, as they contain multiple passes. #38

  • Improve python compatibility by providing a stable signature for user generated functions. #106

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

  • Support constant negative step sizes in @for_loop loops. #129

Breaking changes

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 MemRefView. C++ implementations of the interface can iterate through and directly store results into the MemRefView independant 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

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 resticted 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.

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