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.

翻译：在过去十年中，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的推动。然而，随着我们进入后摩尔定律时代、逼近半导体制造的物理极限，这些应用的未来发展面临疑问。与此同时，量子计算取得显著进展，展现出突破极限的潜力。IBM、Google、Microsoft等主要公司提供了含噪中等规模量子（NISQ）计算机的访问权限。尽管Shor算法和Grover算法具有理论优势，但在当前量子设备上的实际实现仍面临挑战，例如需要额外资源和高数量的受控操作。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——一种在量子-经典混合框架内构建的资源高效型索引值搜索系统。基于Grover算法，ReSaQuS采用自动管理的迭代搜索方法：分析问题规模、过滤低概率数据点，并逐步缩减数据集以降低量子比特需求。通过Qiskit实现并经过广泛实验评估，ReSaQuS展现出累计量子比特消耗降低高达86.36%、活跃周期减少72.72%的显著效果，充分彰显了其在优化量子计算应用部署中的潜力。