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

TL;DR

This work addresses the practical challenge of running quantum search on Noisy Intermediate-Scale Quantum (NISQ) devices by introducing RESAQuS, a resource-efficient, self-adaptive quantum search framework within a quantum-classical hybrid. Building on Grover's algorithm, RESAQuS iteratively reduces the input dataset by updating index/value mappings and applying self-adaptive clustering to filter non-target data based on state fidelities, thereby lowering the required qubits while preserving target accuracy. The approach is implemented in Qiskit and evaluated against state-of-the-art methods, showing substantial reductions in cumulative-qubit consumption ($\text{CQC}$) up to $86.36\%$ and active periods up to $72.72\%$ in cluster mode, across multiple dataset sizes. These results suggest that RESAQuS can enhance the practicality and scalability of quantum search tasks on current hardware, enabling more efficient deployment of quantum-assisted data processing. The method leverages a clear division of labor between quantum processing (amplitude amplification) and classical post-processing (data reduction and mapping), making it suitable for real-world unstructured search applications in a post-Moore regime.

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.

Figures (10)

• Figure 1: Grover's Algorithm with 3 qubits
• Figure 2: 3-Qubit Example: State Probabilities
• Figure 3: RESAQuS System Architecture
• Figure 4: 15-5 Experiments: (a) Probabilities of GSearch in Iteration-1-Invocation-1 (Left) and Iteration-2-Invocation-2 (Right); (b) Probabilities of IQuCS in Iteration-1-Invocation-1 (Left) and Iteration-3-Invocation-2 (Right); (c) Probabilities of RESAQuS in Iteration-1-Invocation-1 (Left) and Iteration-2-Invocation-2
• Figure 5: 40-15 Experiments: (a) Probabilities of GSearch in Iteration-1-Invocation-1 (Left) and Iteration-2-Invocation-2 (Right); (b) Probabilities of IQuCS in Iteration-1-Invocation-1 (Left) and Iteration-3-Invocation-2 (Right); (c) Probabilities of RESAQuS in Iteration-1-Invocation-1 (Left) and Iteration-2-Invocation-2