对比三种量子编码方法在混合量子机器学习中的表现。
Comparing Quantum Encoding Techniques
- 用基础、振幅、旋转三种编码方式处理手写数字数据。
- 振幅编码在准确率和抗噪性上表现最佳，但资源消耗更高。
- 适合研究量子机器学习编码策略的学者参考。
随着量子计算机能力不断提升，其应用前景日益广阔。例如，量子技术正与经典神经网络结合用于机器学习。为实现此类应用，或用于量子化学模拟、密码学等广泛场景，必须将经典数据通过量子编码转换为量子态。目前存在三种基础编码方法：基底编码、振幅编码和旋转编码，以及多种组合方案。本研究聚焦于混合量子-经典机器学习场景，采用 QuClassi 量子神经网络架构对 MNIST 数据集中的 '3' 和 '6' 数字进行二分类任务，评估了准确率、熵、损失及抗噪性等指标，并综合考虑资源消耗与计算复杂度，系统比较了三种主要编码方法的性能表现。
原文摘要 · Abstract (English)
As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.
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