利用最优相似性度量进行量子数据的机器学习
Machine learning of quantum data using optimal similarity measurements
AI总结：
本文提出了一种利用玻色子量子干涉实现样本最优的量子相似性测量方法，用于高效处理量子数据并实现高精度分类与在线学习。
摘要：
量子机器学习旨在通过评估量子态的功能（如相似性）来在数据处理中获得计算优势，这些功能可能在经典计算中难以计算。然而，为了实现量子优势，必须放弃对单个数据实例的昂贵表征，转而使用高效、直接的相似性评估。在这里，我们展示了一种样本最优、硬件高效的协议，利用玻色子量子干涉来估计量子相似性——即态重叠。该方法的样本复杂度与系统维度无关，并在信息论上最优，最多仅相差一个常数因子。实验上，我们在基于全可编程集成光子处理器的量子计算平台Prakash-1上实现了该方案。通过在芯片上制备并干涉多级态来直接提取其重叠，我们展示了在现实噪声实验中对量子数据的高精度分类和在线学习。我们的结果证明了联合重叠测量作为高效量子数据分析的可扩展途径，并为网络集成的量子机器学习提供了实用的构建模块。
英文摘要：
Quantum machine learning seeks a computational advantage in data processing by evaluating functions of quantum states, such as their similarity, that can be classically intractable to compute. For quantum advantage to be possible, however, it is essential to bypass costly characterisation of individual data instances in favour of efficient, direct similarity evaluation. Here we demonstrate a sample-optimal, hardware-efficient protocol for estimating quantum similarity -- the state overlap -- using bosonic quantum interference. The sample complexity of this approach is independent of the system dimension and is information-theoretically optimal up to a constant factor. Experimentally, we implement the scheme on \emph{Prakash-1}, a quantum computing platform based on a fully programmable integrated photonic processor. By preparing and interfering qudit states on the chip to directly extract their overlap, we demonstrate classification and online learning of quantum data with high accuracy in realistic noisy experiments. Our results establish joint overlap measurements as a scalable pathway to efficient quantum data analysis and a practical building block for network-integrated quantum machine learning.
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