Quantum search on noisy intermediate-scale quantum devices

TL;DR

This paper improves quantum search algorithms for noisy intermediate-scale quantum (NISQ) devices by optimizing implementation and benchmarking on various quantum processors, demonstrating enhanced success probabilities and error-aware designs.

Contribution

It introduces error-aware quantum search algorithms tailored for NISQ devices and provides detailed benchmarks on multiple quantum hardware platforms.

Findings

Achieved higher success probability on five-qubit search on NISQ devices.

Demonstrated the feasibility of error-aware quantum search algorithms.

Benchmark results show improved performance over previous implementations.

Abstract

Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Therefore, Grover's algorithm can be improved for NISQ devices. In this paper, we demonstrate how to implement quantum search algorithms better on NISQ devices. We present detailed benchmarks of the five-qubit quantum search algorithm on different quantum processors, including IBMQ, IonQ, and Honeywell quantum devices. We report the highest success probability of the five-qubit search algorithm compared to previous works. Our results show that designing the error-aware quantum search algorithms is possible, which can maximally harness the power of NISQ computers.