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

• URL: http://arxiv.org/abs/2405.04490v1
• Date: Tue, 7 May 2024 17:00:19 GMT
• Title: Resource-Efficient and Self-Adaptive Quantum Search in a Quantum-Classical Hybrid System
• Authors: Zihao Jiang, Zefan Du, Shaolun Ruan, Juntao Chen, Yong Wang, Long Cheng, Rajkumar Buyya, Ying Mao,
• Abstract summary: We introduce ReSaQuS, a resource-efficient index-value searching system within a quantum-classical hybrid framework. We show that ReSaQuS has demonstrated a substantial reduction, up to 86.36% in cumulative qubit consumption and 72.72% in active periods.
• Score: 24.67144593838334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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