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arXiv 2408.12349quant-ph

含噪声量子硬件多体纠缠的机器学习认证

Machine-learning certification of multipartite entanglement for noisy quantum hardware

Andreas J. C. Fuchs, Eric Brunner, Jiheon Seong, Hyeokjea Kwon, Seungchan Seo, Joonwoo Bae, Andreas Buchleitner, Edoardo G. Carnio

AI总结:

针对多体态可分性难题,提出基于随机局部测量统计与非线性降维的纠缠认证流程,经模拟数据验证后成功应用于IBM量子硬件。

摘要:

纠缠是量子物理的基本方面,在概念上和许多应用中均如此。将任意多体态分类为纠缠态或可分态——这一任务被称为可分性问题——构成了重大挑战,因为一个态可能相对于其多种不同的划分而纠缠。我们开发了一种认证流程,将随机局部测量的统计信息输入到非线性降维算法中,以确定给定量子态相对于哪些划分是纠缠的。在针对随机生成的、在不同划分中纠缠且纯度各异的量子态训练模型后,我们在模拟测试数据上验证了其预测的准确性,并最终将其应用于IBM量子计算硬件上制备的态。

英文摘要:

Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware.

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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