用热浴算法冷却提升量子机器学习采样效率。
Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling
- 将监督学习视为降温过程，设计量子制冷协议。
- 无需格罗弗迭代或相位估计算法，降低计算开销。
- 适合噪声中等规模量子设备，实用性强。
本文提出一种基于量子热力学的改进方法，以提升量子机器学习（QML）中的采样效率。将量子监督学习概念化为热力学冷却过程，基于此构建量子制冷协议，在训练和预测中增强样本效率，无需格罗弗迭代或量子相位估计。受热浴算法冷却启发，该方法通过交替熵压缩与热化步骤，降低量子比特熵，提高向主导偏置的极化度。该技术有效减少分类得分与梯度估算的计算开销，为兼容噪声中等规模量子设备的QML算法提供了一种高效实用的解决方案。
原文摘要 · Abstract (English)
This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this concept, we develop a quantum refrigerator protocol that enhances sample efficiency during training and prediction without the need for Grover iterations or quantum phase estimation. Inspired by heat-bath algorithmic cooling protocols, our method alternates entropy compression and thermalization steps to decrease the entropy of qubits, increasing polarization towards the dominant bias. This technique minimizes the computational overhead associated with estimating classification scores and gradients, presenting a practical and efficient solution for QML algorithms compatible with noisy intermediate-scale quantum devices.
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