.png){kind=icon}
返回
TNQMetro: Tensor-network based package for efficient quantum metrology computations
.png){kind=icon}
OA
AI
DOI：10.1016/j.cpc.2021.108282.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
TNQMetro is a numerical package written in Python for calculations of fundamental quantum bounds on measurement precision. Thanks to the usage of the tensor-network formalism it can beat the curse of dimensionality and provides an efficient framework to calculate bounds for finite size system as well as determine the asymptotic scaling of precision in systems where quantum enhancement amounts to a constant factor improvement over the Standard Quantum Limit. It is written in a user-friendly way so that the basic functions do not require any knowledge of tensor networks. Program summary Program Title: TNQMetro CPC Library link to program files: https://doi.org/10.17632/wmw9xrxwgf.1 Developer's repository link: https://github.com/kchabuda/TNQMetro Code Ocean capsule: https://codeocean.com/capsule/7858507 Licensing provisions: MIT Programming language: Python Nature of problem: Exponential growth of the Hilbert space dimension with the number of particles involved is a serious roadblock for numerical studies of the potential of quantum enhanced metrology. It leads to an exponential growth of the computational complexity of even most elementary quantum mechanical calculations, not to mention more advanced computational tasks, such as the ones required for studying the metrological potential of quantum states, e.g. computation of the quantum Fisher information (QFI). Solution method: Thanks to the use of the tensor-network formalism, where quantum states are represented as matrix product states and operators as matrix product operators, it is possible to obtain an efficient description where space complexity scales linearly with the number of elementary particles constituting the physical system. Furthermore, it is possible to efficiently optimize QFI over quantum states and operators in those representations, applying the ideas presented in [1]. This allows to study sophisticated quantum metrological models that are beyond the grasp of the standard numerical methods utilizing the full Hilbert space representation of quantum states and operations. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Quantum metrology
Tensor-network
Matrix product state
Matrix product operator
Python
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.2W
被引数:
3.7W
机构
引用论文
Tensor-network approach for quantum metrology in many-body quantum systems.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏General framework for estimating the ultimate precision limit in noisy quantum-enhanced metrologyNATURE PHYSICSOAAI
NATURE PHYSICSIF18.4
OAAI分享收藏Zn-containing Adhesives Facilitate Collagen Protection and Remineralization at the Resin-Dentin Interface: A Narrative ReviewPolymersOAAI
PolymersIF0
OAAI分享收藏A practical introduction to tensor networks: Matrix product states and projected entangled pair states张量网络的实用介绍: 矩阵乘积状态和投影纠缠对状态OAAI
OAAI分享收藏
查看更多内容