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.

翻译：量子机器学习因其解决计算难题的潜力而备受关注。然而，这些算法能否有效解决实际问题并超越经典算法，尤其是在当前量子硬件上，仍然是一个关键问题。在本工作中，我们提出了一种新颖的量子机器学习方法，称为参数高效量子异常检测（PEQAD），用于实际图像异常检测，旨在实现参数高效性并相较于经典模型获得更高的准确率。仿真结果表明，与经典基线方法相比，PEQAD展现出良好的识别能力，在基准测试中以显著更少的可训练参数实现了超过90%的平均准确率。理论分析证实，PEQAD具有与经典方法相当的表达能力，同时仅需其一小部分参数。此外，我们在超导量子处理器上首次实现了针对通用图像数据集的量子异常检测方法。具体而言，我们在该设备上仅使用16个参数就实现了超过80%的准确率，这为PEQAD在噪声中尺度量子时代的实际可行性提供了初步证据，并突显了其在参数需求方面的显著降低。