Catalyst¶
- Release
0.10.0-dev8
Catalyst is an experimental package that enables just-in-time (JIT) compilation of PennyLane programs. Compile the entire quantum-classical workflow.
Catalyst is currently under heavy development — if you have suggestions on the API or use-cases you’d like to be covered, please open an GitHub issue or reach out. We’d love to hear about how you’re using the library, collaborate on development, or integrate additional devices and frontends.
Key Features¶
Compile the entire quantum-classical workflow, including any optimization loops.
Use Catalyst alongside PennyLane directly from Python. Simply decorate quantum code and hybrid functions with
@qjit, leading to significant performance improvements over standard Python execution.Access advanced control flow that supports both quantum and classical instructions.
Infrastructure for both quantum and classical compilation, allowing you to compile quantum circuits that contain control flow.
Built to be end-to-end differentiable.
Support for PennyLane-Lightning high performance simulators, and Amazon Braket devices. Additional hardware support, including QPUs, to come.
Overview¶
Catalyst currently consists of the following components:
-
The core Catalyst compiler is built using MLIR, with the addition of a quantum dialect used to represent quantum instructions. This allows for a high-level intermediate representation of the classical and quantum components of the program, resulting in advantages during optimization. Once optimized, the compiler lowers the representation down to LLVM + QIR, and a machine binary is produced.
-
The runtime is a C++ runtime with multiple-device support based on QIR that enables the execution of Catalyst-compiled quantum programs. A complete list of all backend devices along with the quantum instruction set supported by these runtime implementations can be found by visiting the runtime documentation.
In addition, we also provide a Python frontend for PennyLane and JAX:
-
A Python library that provides a
@qjitdecorator to just-in-time compile PennyLane hybrid quantum-classical programs. In addition, the frontend package provides Python functions for defining Catalyst-compatible control flow structures, gradient, and mid-circuit measurement.
Authors¶
Catalyst is the work of many contributors.
If you are doing research using Catalyst, please cite our paper:
@article{
Ittah2024,
doi = {10.21105/joss.06720},
url = {https://doi.org/10.21105/joss.06720},
year = {2024},
publisher = {The Open Journal},
volume = {9},
number = {99},
pages = {6720},
author = {David Ittah and Ali Asadi and Erick Ochoa Lopez and Sergei Mironov and Samuel Banning and Romain Moyard and Mai Jacob Peng and Josh Izaac},
title = {Catalyst: a Python JIT compiler for auto-differentiable hybrid quantum programs},
journal = {Journal of Open Source Software}
}