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基于随机测量的高维片上双光子频率梳的贝叶斯层析成像

Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements.

作者信息

Lu Hsuan-Hao, Myilswamy Karthik V, Bennink Ryan S, Seshadri Suparna, Alshaykh Mohammed S, Liu Junqiu, Kippenberg Tobias J, Leaird Daniel E, Weiner Andrew M, Lukens Joseph M

机构信息

Quantum Information Science Section, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.

School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN, 47907, USA.

出版信息

Nat Commun. 2022 Jul 27;13(1):4338. doi: 10.1038/s41467-022-31639-z.

Abstract

Owing in large part to the advent of integrated biphoton frequency combs, recent years have witnessed increased attention to quantum information processing in the frequency domain for its inherent high dimensionality and entanglement compatible with fiber-optic networks. Quantum state tomography of such states, however, has required complex and precise engineering of active frequency mixing operations, which are difficult to scale. To address these limitations, we propose a solution that employs a pulse shaper and electro-optic phase modulator to perform random operations instead of mixing in a prescribed manner. We successfully verify the entanglement and reconstruct the full density matrix of biphoton frequency combs generated from an on-chip SiN microring resonator in up to an 8 × 8-dimensional two-qudit Hilbert space, the highest dimension to date for frequency bins. More generally, our employed Bayesian statistical model can be tailored to a variety of quantum systems with restricted measurement capabilities, forming an opportunistic tomographic framework that utilizes all available data in an optimal way.

摘要

在很大程度上由于集成双光子频率梳的出现,近年来,频域中的量子信息处理因其固有的高维性以及与光纤网络兼容的纠缠特性而受到越来越多的关注。然而,对这类量子态进行量子态层析成像需要对有源频率混合操作进行复杂而精确的工程设计,而这很难实现规模化。为了克服这些限制,我们提出了一种解决方案,即使用脉冲整形器和电光相位调制器来执行随机操作,而不是按规定方式进行混合。我们成功验证了纠缠,并在高达8×8维的双量子比特希尔伯特空间中重建了由片上氮化硅微环谐振器产生的双光子频率梳的完整密度矩阵,这是迄今为止频率 bins 的最高维度。更一般地说,我们采用的贝叶斯统计模型可以针对具有受限测量能力的各种量子系统进行定制,形成一个以最优方式利用所有可用数据的机会主义层析成像框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6bc/9329349/24f701319b9b/41467_2022_31639_Fig1_HTML.jpg

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