A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor

TL;DR

This paper introduces PEQAD, a quantum anomaly detection method that is parameter-efficient, achieves high accuracy on image datasets, and is practically implemented on a superconducting quantum processor, demonstrating its viability in current quantum hardware.

Contribution

The paper presents PEQAD, a novel quantum anomaly detection algorithm that is both parameter-efficient and practically implementable on existing quantum hardware, with competitive accuracy.

Findings

PEQAD achieves over 90% accuracy on benchmark datasets.

The method requires significantly fewer parameters than classical models.

First implementation of quantum anomaly detection on a superconducting quantum processor.

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 novel 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 favourable recognition capabilities compared to classical baselines, achieving an average accuracy of over 90% on benchmarks with significantly fewer trainable parameters. Theoretical analysis confirms 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 in the noisy intermediate-scale quantum era and highlighting its significant reduction in parameter requirements.