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通过桥接离散变量和连续变量编码来纠缠薛定谔猫态

Entangling Schrödinger's cat states by bridging discrete- and continuous-variable encoding.

作者信息

Hoshi Daisuke, Nagase Toshiaki, Kwon Sangil, Iyama Daisuke, Kamiya Takahiko, Fujii Shiori, Mukai Hiroto, Ahmed Shahnawaz, Kockum Anton Frisk, Watabe Shohei, Yoshihara Fumiki, Tsai Jaw-Shen

机构信息

Department of Physics, Graduate School of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan.

RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama 351-0198, Japan.

出版信息

Nat Commun. 2025 Feb 3;16(1):1309. doi: 10.1038/s41467-025-56503-8.

DOI:10.1038/s41467-025-56503-8
PMID:39900944
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11791159/
Abstract

In quantum information processing, two primary research directions have emerged: one based on discrete variables (DV) and the other on the structure of quantum states in a continuous-variable (CV) space. Integrating these two approaches could unlock new potentials, overcoming their respective limitations. Here, we show that such a DV-CV hybrid approach, applied to superconducting Kerr parametric oscillators (KPOs), enables us to entangle a pair of Schrödinger's cat states by two methods. The first involves the entanglement-preserving conversion between Bell states in the Fock-state basis (DV encoding) and those in the cat-state basis (CV encoding). The second method implements a gate between two cat states following the procedure for Fock-state encoding. This simple and fast gate operation completes a universal quantum gate set in a KPO system. Our work offers powerful applications of DV-CV hybridization and marks a first step toward developing a multi-qubit platform based on planar KPO systems.

摘要

在量子信息处理领域,已出现两个主要研究方向:一个基于离散变量(DV),另一个基于连续变量(CV)空间中的量子态结构。整合这两种方法可能会释放新的潜力,克服它们各自的局限性。在此,我们表明,将这种DV-CV混合方法应用于超导克尔参量振荡器(KPO),能够通过两种方法使一对薛定谔猫态纠缠。第一种方法涉及在福克态基(DV编码)中的贝尔态与猫态基(CV编码)中的贝尔态之间进行保持纠缠的转换。第二种方法按照福克态编码的程序在两个猫态之间实现一个门操作。这种简单快速的门操作在KPO系统中完成了一个通用量子门集。我们的工作展示了DV-CV混合的强大应用,并标志着朝着基于平面KPO系统开发多量子比特平台迈出了第一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/f8a00c5e8d5f/41467_2025_56503_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/fc330ceaae6e/41467_2025_56503_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/e2b4ee46bfa2/41467_2025_56503_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/856a3d39118a/41467_2025_56503_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/f8a00c5e8d5f/41467_2025_56503_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/fc330ceaae6e/41467_2025_56503_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/e2b4ee46bfa2/41467_2025_56503_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/856a3d39118a/41467_2025_56503_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ff/11791159/f8a00c5e8d5f/41467_2025_56503_Fig4_HTML.jpg

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