量子增强模式识别
Quantum-enhanced pattern recognition
AI总结：
本文首次在多单元模式识别中展示量子优势，利用纠缠探针态和光子计数，结合经典后处理，在MNIST数据集上实现分类错误率优于经典方法，验证了量子增强模式识别的可行性。
摘要：
模式识别的挑战在于制定一种能够准确提取数据集特征并对其样本进行分类的策略。在现实场景中，该数据集可能是一个我们想要从中检索信息的物理系统，例如光学经典存储器的读出。量子读取的理论和实验发展已经证明，通过使用量子资源（即纠缠输入态），光学存储器的读出可以比最佳经典策略获得显著增强。然而，这种量子优势的实用性取决于量子读取的可扩展性，迄今为止其实验演示仅限于单个单元。在这项工作中，我们首次在多单元模式识别问题中展示了量子优势。通过对MNIST手写数字数据集中的数字进行实验实现，并应用先进的经典后处理，我们报告了使用纠缠探针态和光子计数在分类错误方面相对于经典资源所实现的量子优势，证实了通过量子传感器获得的优势可以在整个模式识别和复杂后处理过程中得以维持。这激励了未来在复杂领域内对玻色子损耗进行量子增强模式识别的发展。
英文摘要：
The challenge of pattern recognition is to invoke a strategy that can accurately extract features of a dataset and classify its samples. In realistic scenarios this dataset may be a physical system from which we want to retrieve information, such as in the readout of optical classical memories. The theoretical and experimental development of quantum reading has demonstrated that the readout of optical memories can be dramatically enhanced through the use of quantum resources (namely entangled input-states) over that of the best classical strategies. However, the practicality of this quantum advantage hinges upon the scalability of quantum reading, and up to now its experimental demonstration has been limited to individual cells. In this work, we demonstrate for the first time quantum advantage in the multi-cell problem of pattern recognition. Through experimental realizations of digits from the MNIST handwritten digit dataset, and the application of advanced classical post-processing, we report the use of entangled probe states and photon-counting to achieve quantum advantage in classification error over that achieved with classical resources, confirming that the advantage gained through quantum sensors can be sustained throughout pattern recognition and complex post-processing. This motivates future developments of quantum-enhanced pattern recognition of bosonic-loss within complex domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
