A cross-platform execution engine for the quantum intermediate representation

2024-04-22

Journal of Supercomputing

AI Summary

This work addresses the challenge of cross-platform execution for quantum-classical hybrid programs. Methodologically, we propose QIR-EE—the first general-purpose Quantum Intermediate Representation (QIR) execution engine—built upon LLVM to establish a scalable execution framework; it performs semantic mapping from QIR to LLVM IR, introduces a hardware-agnostic API abstraction layer with modular extension points, and enables unified orchestration across heterogeneous quantum hardware (IonQ, Quantinuum, IBM) and numerical simulators; it further supports quantum-classical memory sharing and joint control-flow execution. Our key contributions are: (1) the first general-purpose QIR execution architecture, decoupling program execution from vendor-specific hardware; and (2) empirical validation demonstrating significantly improved hybrid instruction throughput and end-to-end framework latency under 50 ms, with seamless interoperability across mainstream development toolchains including Q# and PyQuil.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

Problem

• Developing cross-platform execution engine for quantum intermediate representation
• Executing hybrid quantum-classical programs across multiple hardware platforms
• Integrating quantum computing frameworks to handle mixed instructions and data

Innovation

• Uses LLVM to execute hybrid quantum instructions
• Provides extension points for customized runtime environments
• Integrates with XACC library for cross-platform quantum execution