Dimension reduction in quantum sampling of stochastic processes

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Chengran Yang, Marta Florido-Llinàs, Mile Gu, Thomas J. Elliott
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Abstract

Quantum technologies offer a promising route to the efficient sampling and analysis of stochastic processes, with potential applications across the sciences. Such quantum advantages rely on the preparation of a quantum sample state of the stochastic process, which requires a memory system to propagate correlations between the past and future of the process. Here, we introduce a method of lossy quantum dimension reduction that allows this memory to be compressed, not just beyond classical limits, but also beyond current state-of-the-art quantum stochastic sampling approaches. We investigate the trade-off between the saving in memory resources from this compression, and the distortion it introduces. We show that our approach can be highly effective in low distortion compression of both Markovian and strongly non-Markovian processes alike. We further discuss the application of our results to quantum stochastic modelling more broadly.

Abstract Image

随机过程量子采样中的降维问题
量子技术为随机过程的有效采样和分析提供了一条有前途的途径,在各个科学领域都有潜在的应用。这种量子优势依赖于随机过程的量子样本状态的制备,这需要一个存储系统来传播过程的过去和未来之间的相关性。在这里,我们介绍了一种有损量子降维的方法,该方法不仅超越了经典限制,而且超越了当前最先进的量子随机抽样方法。我们研究了这种压缩所节省的内存资源和它所带来的失真之间的权衡。我们证明了我们的方法在马尔可夫过程和强非马尔可夫过程的低失真压缩中都是非常有效的。我们进一步讨论了我们的结果更广泛地应用于量子随机建模。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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