Improving adiabatic quantum factorization via chopped random-basis optimization

TL;DR

This paper demonstrates that the chopped random-basis (CRAB) optimization technique significantly improves the fidelity of adiabatic quantum factorization algorithms, especially in noisy, near-term quantum devices, by enhancing performance on integers from 21 to 2479.

Contribution

The study introduces CRAB optimization to adiabatic quantum algorithms, showing its effectiveness in improving factorization fidelity under realistic noise conditions.

Findings

CRAB enhances adiabatic quantum factorization fidelity.

Performance remains robust under dephasing noise.

Effective for integers up to 2479.

Abstract

Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current NISQ devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB's practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks.