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arXiv 1902.07754quant-ph
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N-qubit系统的实验成对纠缠估计：量子硬件编程的机器学习方法

Experimental pairwise entanglement estimation for an N-qubit system :A machine learning approach for programming quantum hardware

N. L. Thompson, N. H. Nguyen, E. C. Behrman, J. E. Steck

备注：
Submitted to Quantum Information Processing
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摘要：

设计和实现大中型量子计算机的算法并不容易。在以前的工作中，我们已经提出并发展了使用机器学习技术来训练量子系统的想法，使得所需的过程被“学习”，从而避免了算法设计的困难。这对于小型系统来说非常有效。但目标是宏观物理计算。在这里，我们实现了我们的学习成对纠缠见证微软的Q#，一个商业上可用的门模型量子计算机模拟器，我们进行统计分析，以确定可靠性和可重复性，我们表明，使用机器学习技术称为“自举”，我们可以推断出模式介观N从模拟结果为三，四，五，六，七量子比特系统。我们的研究结果为通用量子计算机算法的设计和计算提供了一条富有成效的途径。

英文摘要：

Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is "learned," thus obviating the algorithm design difficulty. This works quite well for small systems. But the goal is macroscopic physical computation. Here, we implement our learned pairwise entanglement witness on Microsoft's Q#, one of the commercially available gate model quantum computer simulators; we perform statistical analysis to determine reliability and reproduceability; and we show that using the machine learning technique called "bootstrapping", we can infer the pattern for mesoscopic N from simulation results for three-, four-, five-, six-, and seven-qubit systems. Our results suggest a fruitful pathway for general quantum computer algorithm design and computation.

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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