[论文解读] Optimization Applications as Quantum Performance Benchmarks

Thomas Lubinski, Carleton Coffrin+4 more|arXiv (Cornell University)|Feb 5, 2023

Quantum Computing Algorithms and Architecture被引用 7

一句话总结

该论文开发并展示了一个开源基准框架，用于在跨异构硬件上评估量子优化求解器（QAOA 和量子退火）在最大割（Max-Cut）问题上的表现，聚焦解质量与运行时间之间的权衡。

ABSTRACT

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.

研究动机与目标

• Develop a methodology to evaluate the performance of quantum computers running optimization solvers on heterogeneous platforms.
• Implement an open-source benchmark procedure that integrates with the QED-C framework and supports iterative quantum-classical hybrid algorithms.
• Demonstrate the framework using Max-Cut with QAOA and QA to yield application-focused performance insights.
• Present visualization and analysis techniques that reveal throughput and quality trade-offs relevant to optimization tasks.

提出的方法

• Define benchmark algorithms for QAOA and QA that operate over a range of problem sizes.
• Encode Max-Cut instances (3-regular graphs) into quantum circuits for QAOA and QA benchmark runs.
• Measure application-specific quality metrics such as approximation ratio and optimality gap, alongside run-time metrics like elapsed execution time and circuit depth.
• Use restart loops and parameter optimization to explore the solution landscape within time limits.
• Aggregate results into performance profiles that illustrate quality-versus-time trade-offs across backends.
• Ensure compatibility with the open-source QED-C benchmark suite for cross-backend comparability.

实验结果

研究问题

• RQ1 How does solution quality (e.g., approximation ratio) evolve with execution time for QAOA and QA on Max-Cut problems?
• RQ2 What factors influence ansatz fidelity and throughputs on gate-model and annealing quantum hardware when solving combinatorial optimization problems?
• RQ3 Can the enhanced benchmark framework provide clear, comparable performance profiles across diverse quantum backends?
• RQ4 How do throughput considerations affect the total cost of using quantum optimization solvers in iterative hybrid pipelines?

主要发现

• The framework yields performance profiles that depict the trade-off between result quality and execution time for QAOA and QA on Max-Cut across problem sizes.
• An open-source benchmarking procedure was implemented and demonstrated within the QED-C benchmark ecosystem, enabling cross-backend analysis.
• Benchmark results on quantum simulators validate expected behavior and illustrate how results can be analyzed to extract insights about throughput and quality.
• The study identifies variables that impact quantum-accelerated optimization performance and demonstrates how the framework can adapt to different quantum technologies.
• The approach showcases the framework’s ability to visualize and compare iterative quantum-classical optimization performance in a familiar Operations Research context.