Work

Parameter-efficient quantum anomaly detection method on a superconducting quantum processor

Year: 2025

Type: article

Abstract: Quantum machine learning has gained attention for its potential to address computational challenges. However, whether those algorithms can effectively solve practical problems and outperform their classical counterparts, especially on current quantum hardware, remains a critical question. In this work, we propose a quantum machine learning method, called Parameter-Efficient Quantum Anomaly Detection (PEQAD), for practical image anomaly detection, which aims to achieve both parameter efficiency and superior accuracy compared to classical models. Emulation results indicate that PEQAD demonstrates favorable recognition capabilities compared to classical baselines, achieving an average accuracy of over 90% on benchmarks with significantly fewer trainable parameters. Theoretical analysis suggests that PEQAD has a comparable expressivity to classical counterparts while requiring only a fraction of the parameters. Furthermore, we demonstrate the first implementation of a quantum anomaly detection method for general image datasets on a superconducting quantum processor. Specifically, we achieve an accuracy of over 80% with only 16 parameters on the device, providing initial evidence of PEQAD’s practical viability and highlighting its significant reduction in parameter requirements.

Source: Physical Review Research

DOI: 10.1103/cv9y-2cnj

Authors: Maida Wang, Jinyang Jiang, Peter V. Coveney

Institutions: University College London, Peking University, University of Amsterdam

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: University College London, Chinese Academy of Sciences, China Scholarship Council, Engineering and Physical Sciences Research Council