利用压缩感知实现变分量子算法的高性能调试
Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing
AI总结：
本文提出 OSCAR 工具，利用压缩感知以最高 100 倍加速重建变分量子算法的代价景观，帮助配置初始化、噪声缓解和经典优化器，实现 NISQ 硬件上的高效调试与性能调优。
摘要：
变分量子算法（VQAs）有潜力利用当代含噪声中等规模量子（NISQ）计算机解决实际问题。VQAs 通过对参数化量子电路计算的损失函数进行经典优化，在存在量子比特错误的情况下寻找近最优解。然而，由于量子硬件的可用性有限、错误率高以及经典模拟开销巨大，开发和测试 VQAs 面临很大挑战。此外，VQA 研究人员必须为电路参数选择正确的初始化方式，使用合适的经典优化器配置，并部署适当的错误缓解方法。遗憾的是，目前这些任务都是以临时、非系统化的方式完成的，因为还没有软件工具能够配置和调优 VQA 的超参数。本文提出了 OSCAR（基于压缩感知的代价景观重建，cOmpressed Sensing based Cost lAndscape Reconstruction），用于帮助配置：1）正确的初始化，2）噪声缓解技术，以及 3）经典优化器，从而在 NISQ 硬件上最大化解的质量。OSCAR 通过向用户提供损失函数景观而无需像网格搜索那样运行数千个量子电路，实现了高效的调试和性能调优。利用 OSCAR，我们能够以高达 100 倍的加速准确重建完整的代价景观。此外，OSCAR 可以通过对已计算景观进行插值来即时计算优化器函数查询，从而以显著降低的开销支持对 VQA 配置进行试运行。
英文摘要：
Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
