论文标题
部分可观测时空混沌系统的无模型预测
Demonstration of a bosonic quantum classifier with data re-uploading
论文作者
论文摘要
在单个Qubit系统中，可以使用数据重载技术实现通用量子分类器。在这项研究中，我们提出了一种新的量子分类器，该分类器将此技术应用于核系统，并使用硅光学整合量子电路成功证明了它。我们建立了一种适用于玻感系统的量子机学习算法的理论，并实施了可编程光电路与干涉仪结合使用。在原理实验证明的证明中，使用无关的两光子的一部分学习和分类的学习和分类导致繁殖率约为94 \％。由于可以将此方法应用于任意的两种模式N-Photon系统，因此将来有望将光学量子分类器的进一步开发（例如扩展到量子纠缠和多光子状态）。
In a single qubit system, a universal quantum classifier can be realised using the data-reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrated it using silicon optical integrated quantum circuits. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two-photons resulted in a classification with a reproduction rate of approximately 94\% in the proof of principle experiment. As this method can be applied to arbitrary two-mod N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multi-photon states, is expected in the future.
