Hybrid Programming for Near-term Quantum Computing Systems

TL;DR

This paper discusses the challenges of hybrid classical-quantum programming on near-term quantum systems and introduces the XACC framework to address these issues, demonstrated through nuclear physics simulations.

Contribution

It presents the XACC framework as a hardware-agnostic solution for hybrid quantum programming, compilation, and execution challenges.

Findings

XACC effectively manages diverse QPU programming workflows.

The framework improves reliability and portability of quantum applications.

Demonstrated success in nuclear physics simulations.

Abstract

Recent computations involving quantum processing units (QPUs) have demonstrated a series of challenges inherent to hybrid classical-quantum programming, compilation, execution, and verification and validation. Despite considerable progress, system-level noise, limited low-level instructions sets, remote access models, and an overall lack of portability and classical integration presents near-term programming challenges that must be overcome in order to enable reliable scientific quantum computing and support robust hardware benchmarking. In this work, we draw on our experience in programming QPUs to identify common concerns and challenges, and detail best practices for mitigating these challenge within the current hybrid classical-quantum computing paradigm. Following this discussion, we introduce the XACC quantum compilation and execution framework as a hardware and language agnostic solution that addresses many of these hybrid programming challenges. XACC supports extensible methodologies for managing a variety of programming, compilation, and execution concerns across the increasingly diverse set of QPUs. We use recent nuclear physics simulations to illustrate how the framework mitigates programming, compilation, and execution challenges and manages the complex workflow present in QPU-enhanced scientific applications. Finally, we codify the resulting hybrid scientific computing workflow in order to identify key areas requiring future improvement.