A complexity transition in displaced Gaussian Boson sampling

IF 8.3 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Zhenghao Li, Naomi R. Solomons, Jacob F. F. Bulmer, Raj B. Patel, Ian A. Walmsley
{"title":"A complexity transition in displaced Gaussian Boson sampling","authors":"Zhenghao Li, Naomi R. Solomons, Jacob F. F. Bulmer, Raj B. Patel, Ian A. Walmsley","doi":"10.1038/s41534-025-01062-5","DOIUrl":null,"url":null,"abstract":"<p>Gaussian Boson Sampling (GBS) is the problem of sampling from the output of photon-number-resolving measurements of squeezed states input to a linear optical interferometer. For purposes of demonstrating quantum computational advantage as well as practical applications, a large photon number is often desirable. However, producing squeezed states with high photon numbers is experimentally challenging. In this work, we examine the computational complexity implications of increasing the photon number by introducing coherent states. This displaces the state in phase space and as such we call this modified problem <i>Displaced GBS</i>. By utilising a connection to the matching polynomial in graph theory, we first describe an efficient classical algorithm for Displaced GBS when displacement is high or when the output state is represented by a non-negative graph. Then we provide complexity theoretic arguments for the quantum advantage of the problem in the low-displacement regime and numerically quantify where the complexity transition occurs.</p>","PeriodicalId":19212,"journal":{"name":"npj Quantum Information","volume":"693 1","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Quantum Information","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1038/s41534-025-01062-5","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

Gaussian Boson Sampling (GBS) is the problem of sampling from the output of photon-number-resolving measurements of squeezed states input to a linear optical interferometer. For purposes of demonstrating quantum computational advantage as well as practical applications, a large photon number is often desirable. However, producing squeezed states with high photon numbers is experimentally challenging. In this work, we examine the computational complexity implications of increasing the photon number by introducing coherent states. This displaces the state in phase space and as such we call this modified problem Displaced GBS. By utilising a connection to the matching polynomial in graph theory, we first describe an efficient classical algorithm for Displaced GBS when displacement is high or when the output state is represented by a non-negative graph. Then we provide complexity theoretic arguments for the quantum advantage of the problem in the low-displacement regime and numerically quantify where the complexity transition occurs.

Abstract Image

位移高斯玻色子采样中的复杂性跃迁
高斯玻色子采样(GBS)是将压缩态的光子数分辨测量结果的输出采样到线性光学干涉仪的问题。为了展示量子计算的优势以及实际应用,通常需要大的光子数。然而,产生具有高光子数的压缩态在实验上具有挑战性。在这项工作中,我们研究了通过引入相干态来增加光子数的计算复杂性。这在相空间中置换了状态,因此我们称这个修正问题为置换GBS。利用图论中与匹配多项式的联系,首先描述了位移大或输出状态为非负图时位移GBS的有效经典算法。在此基础上,对该问题在低位移状态下的量子优势进行了复杂性理论论证,并对复杂性跃迁发生的位置进行了数值量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信