Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation

TL;DR

This paper introduces a Python extension that leverages quantum just-in-time compilation to enable efficient, hardware-agnostic quantum-classical computing, addressing performance limitations of rapid prototyping in quantum software development.

Contribution

It presents a novel Python extension built on QCOR infrastructure for high-performance, heterogeneous quantum-classical computing with a single-source, hardware-agnostic approach.

Findings

Enables tight CPU-QPU integration with high performance

Provides a Pythonic programming model for quantum-classical workflows

Demonstrates utility through practical examples

Abstract

Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly-coupled CPU-QPU architectures with fast-feedback. Here we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardware-agnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Pythonic extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.