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.

翻译：变分量子算法（VQAs）有望利用当代含噪中等规模量子（NISQ）计算机解决实际问题。VQAs通过经典优化由参数化量子电路计算的损失函数，在存在量子比特错误的情况下寻找近优解。然而，由于量子硬件可用性有限、错误率较高以及经典模拟的巨大开销，开发和测试VQAs面临挑战。此外，VQA研究人员必须为电路参数选择合适的初始化方法，利用合适的经典优化器配置，并部署适当的错误缓解措施。遗憾的是，当前这些任务均以临时方式完成，缺乏用于配置和调优VQA超参数的软件工具。本文提出OSCAR（基于压缩感知的成本景观重建），以辅助配置：1）正确的初始化方法，2）噪声缓解技术，以及3）经典优化器，从而最大化NISQ硬件上解的质量。OSCAR通过向用户提供损失函数景观（无需像网格搜索那样运行数千个量子电路）来实现高效的调试与性能调优。利用OSCAR，我们能够以高达100倍的加速比精确重建完整的成本景观。此外，OSCAR可通过插值计算出的景观即时执行优化器函数查询，从而大幅降低VQA配置试运行的开销。