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arXiv 2405.04490cs.DCquant-ph
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量子-经典混合系统中资源高效且自适应的量子搜索

Resource-Efficient and Self-Adaptive Quantum Search in a Quantum-Classical Hybrid System

Zihao Jiang, Zefan Du, Shaolun Ruan, Juntao Chen, Yong Wang, Long Cheng, Rajkumar Buyya, Ying Mao

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AI总结：

本文提出 ReSaQuS，一种量子-经典混合框架下的资源高效索引值搜索系统，通过自动迭代搜索逐步缩减数据集，在实验中最多降低 86.36% 的累计量子比特消耗和 72.72% 的活跃周期。

摘要：

过去十年，深度学习和大数据应用的快速发展得益于海量数据集和高性能计算系统。然而，随着我们逐渐逼近后摩尔定律时代半导体制造的物理极限，这些应用的未来引发了疑问。与此同时，量子计算取得了显著进展，并有望突破这些限制。IBM、Google 和 Microsoft 等主要公司提供了对含噪声中等规模量子（NISQ）计算机的访问。尽管 Shor 算法和 Grover 算法在理论上具有前景，但在当前量子设备上的实际实现仍面临挑战，例如需要额外资源以及大量受控操作。为应对这些挑战并优化有限板载量子比特的利用，我们提出了 ReSaQuS，这是一种在量子-经典混合框架下的资源高效索引值搜索系统。ReSaQuS 以 Grover 算法为基础，采用自动管理的迭代搜索方法。该方法分析问题规模，过滤掉概率较低的数据点，并在量子比特需求递减的情况下逐步缩减数据集。ReSaQuS 使用 Qiskit 实现，并通过大量实验进行评估，结果表明其在累计量子比特消耗上最多降低了 86.36%，在活跃周期上最多降低了 72.72%，进一步证明了其在优化量子计算应用部署方面的潜力。

英文摘要：

Over the past decade, the rapid advancement of deep learning and big data applications has been driven by vast datasets and high-performance computing systems. However, as we approach the physical limits of semiconductor fabrication in the post-Moore's Law era, questions arise about the future of these applications. In parallel, quantum computing has made significant progress with the potential to break limits. Major companies like IBM, Google, and Microsoft provide access to noisy intermediate-scale quantum (NISQ) computers. Despite the theoretical promise of Shor's and Grover's algorithms, practical implementation on current quantum devices faces challenges, such as demanding additional resources and a high number of controlled operations. To tackle these challenges and optimize the utilization of limited onboard qubits, we introduce ReSaQuS, a resource-efficient index-value searching system within a quantum-classical hybrid framework. Building on Grover's algorithm, ReSaQuS employs an automatically managed iterative search approach. This method analyzes problem size, filters fewer probable data points, and progressively reduces the dataset with decreasing qubit requirements. Implemented using Qiskit and evaluated through extensive experiments, ReSaQuS has demonstrated a substantial reduction, up to 86.36\% in cumulative qubit consumption and 72.72\% in active periods, reinforcing its potential in optimizing quantum computing application deployment.

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。

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