用极少参数实现高精度图像异常检测，首次在超导量子处理器上验证。
A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
- 设计参数高效量子模型，仅16个可训练参数即完成检测。
- 在真实量子硬件上达到80%以上准确率，优于经典基线。
- 适合关注量子机器学习落地的科研与工程人员。
量子机器学习因其解决计算难题的潜力受到关注，但其在当前量子硬件上是否能有效应对实际问题并超越经典方法，仍是关键挑战。本文提出一种新型量子机器学习方法——参数高效量子异常检测（PEQAD），用于实际图像异常检测，旨在同时实现参数效率和更高精度。仿真结果显示，相较于经典基线，PEQAD在基准测试中平均准确率超过90%，且可训练参数显著更少。理论分析表明，其表达能力与经典模型相当，但参数量仅为后者的极小部分。此外，本文首次在超导量子处理器上实现了通用图像数据集的量子异常检测，仅用16个参数即获得超过80%的准确率，为该方法在噪声中等规模量子时代中的可行性提供了初步证据，并凸显其参数需求的大幅降低。
原文摘要 · Abstract (English)
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.
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