Iterative Qubits Management for Quantum Index Searching in a Hybrid
  System
Recent advances in quantum computing systems attract tremendous attention.
Commercial companies, such as IBM, Amazon, and IonQ, have started to provide
access to noisy intermediate-scale quantum computers. Researchers and
entrepreneurs attempt to deploy their applications that aim to achieve a
quantum speedup. Grover's algorithm and quantum phase estimation are the
foundations of many applications with the potential for such a speedup. While
these algorithms, in theory, obtain marvelous performance, deploying them on
existing quantum devices is a challenging task. For example, quantum phase
estimation requires extra qubits and a large number of controlled operations,
which are impractical due to low-qubit and noisy hardware. To fully utilize the
limited onboard qubits, we propose IQuCS, which aims at index searching and
counting in a quantum-classical hybrid system. IQuCS is based on Grover's
algorithm. From the problem size perspective, it analyzes results and tries to
filter out unlikely data points iteratively. A reduced data set is fed to the
quantum computer in the next iteration. With a reduction in the problem size,
IQuCS requires fewer qubits iteratively, which provides the potential for a
shared computing environment. We implement IQuCS with Qiskit and conduct
intensive experiments. The results demonstrate that it reduces qubits
consumption by up to 66.2%.
Wenrui Mu Ying Mao Long Cheng Qingle Wang Weiwen Jiang Pin-Yu Chen