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.
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 the Lightning high performance simulator. Additional hardware support, including GPUs and QPUs to come.
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 based on QIR that enables the execution of Catalyst-compiled quantum programs. Currently, a runtime implementation is available for the state-vector simulators Lightning. A complete list of the quantum instruction set supported by these runtime implementations can be found by visiting the Catalyst 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.