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arXiv 2001.05472quant-phcs.LGstat.ML
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噪声量子行走的机器学习传输效率

Machine learning transfer efficiencies for noisy quantum walks

ITMO University(俄罗斯国立信息技术、机械学与光学研究型大学);Valiev Institute of Physics and Technology, Russian Academy of Sciences(俄罗斯科学院瓦列夫物理技术研究所);University of Basel(巴塞尔大学);Moscow Institute of Physics and Technology(莫斯科物理技术学院);National Center for Theoretical Sciences(国家理论科学中心);National Tsing Hua University(国立清华大学)

Alexey A. Melnikov, Leonid E. Fedichkin, Ray-Kuang Lee, Alexander Alodjants

摘要:

已知量子效应在穿过网络的粒子转移中提供优势。为了实现这一优势,必须找到对图类型和量子系统相干性的要求。在这里,我们表明,通过从模拟示例中学习,可以自动找到这些要求的过程。自动化是通过使用特定类型的卷积神经网络来完成的,该卷积神经网络学习理解使用哪个网络以及在哪个相干性要求下量子优势是可能的。我们的机器学习方法被应用于研究不同大小的循环图上的噪声量子行走。我们发现,它是可能的,以预测存在的量子优势的整个退相干参数范围内,即使是训练集以外的图形。我们的研究结果对于证明量子实验的优势具有重要意义,并为自动化科学研究和发现铺平了道路。

英文摘要:

Quantum effects are known to provide an advantage in particle transfer across networks. In order to achieve this advantage, requirements on both a graph type and a quantum system coherence must be found. Here we show that the process of finding these requirements can be automated by learning from simulated examples. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. Our machine learning approach is applied to study noisy quantum walks on cycle graphs of different sizes. We found that it is possible to predict the existence of quantum advantage for the entire decoherence parameter range, even for graphs outside of the training set. Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.

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。

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