用浅层电路在量子处理器上实现高效数据学习，突破训练难与数据加载成本高的瓶颈。
Shallow-circuit Supervised Learning on a Quantum Processor
- 基于线性哈密顿量构建紧凑量子数据表示，降低数据加载开销。
- 在50量子比特的IBM Heron处理器上成功训练，验证方法可扩展性。
- 适合追求低资源消耗量子机器学习的科研与工程人员。
量子计算长期承诺在数据分析中带来变革性进展，但实际量子机器学习仍因经典数据加载成本高、近中期硬件上算法可训练性差等根本障碍而难以实现。本文提出一种基于线性哈密顿量的机器学习方法，通过k局部哈密顿量的基态问题实现经典数据的紧凑量子表示。利用近期提出的基于采样的克雷洛夫量子对角化方法，计算数据哈密顿量的低能态，并通过局部梯度训练其参数以表达经典数据集。我们在使用最多50个量子比特的IBM Heron量子处理器上，对基准数据集进行了实验，验证了该方法的有效性与可扩展性。
原文摘要 · Abstract (English)
Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum machine learning algorithms designed for near-term quantum hardware. In this work, we show that one can overcome these obstacles by using a linear Hamiltonian-based machine learning method which provides a compact quantum representation of classical data via ground state problems for k-local Hamiltonians. We use the recent sample-based Krylov quantum diagonalization method to compute low-energy states of the data Hamiltonians, whose parameters are trained to express classical datasets through local gradients. We demonstrate the efficacy and scalability of the methods by performing experiments on benchmark datasets using up to 50 qubits of an IBM Heron quantum processor.
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