Resource-Efficient and Self-Adaptive Quantum Search in a
  Quantum-Classical Hybrid System
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.
Zihao Jiang Zefan Du Shaolun Ruan Juntao Chen Yong Wang Long Cheng Rajkumar Buyya Ying Mao