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  2. Release 0.3.1

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.