A reinforcement learning approach to the design of quantum chains for optimal energy and state transfer

被引:0
|
作者
Sgroi, S. [1 ,2 ]
Zicari, G. [1 ]
Imparato, A. [3 ,4 ]
Paternostro, M. [1 ,2 ]
机构
[1] Queens Univ Belfast, Ctr Quantum Mat & Technol, Sch Math & Phys, Belfast BT7 1NN, North Ireland
[2] Univ Palermo, Dipartimento Fis & Chim Emilio Segre, via Archirafi 36, I-90123 Palermo, Italy
[3] Univ Trieste, Dept Phys, Str Costiera 11, I-34151 Trieste, Italy
[4] Ist Nazl Fis Nucl, Trieste Sect, I-34127 Trieste, Italy
来源
MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2025年 / 6卷 / 01期
基金
英国工程与自然科学研究理事会;
关键词
reinforcement learning; quantum chains; energy transfer; quantum networks; COHERENCE; DYNAMICS; ARRAY;
D O I
10.1088/2632-2153/ada71d
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a bottom-up approach, based on reinforcement learning, to the design of a chain achieving efficient excitation-transfer performances. We assume distance-dependent interactions among particles arranged in a chain under tight-binding conditions. Starting from two particles and a localised excitation, we gradually increase the number of constitutents of the system so as to improve the transfer probability. We formulate the problem of finding the optimal locations and numbers of particles as a Markov decision process: we use proximal policy optimization to find the optimal chain-building policies and the optimal chain configurations under different scenarios. We consider both the case in which the target is a sink connected to the end of the chain and the case in which the target is the right-most particle in the chain. We address the problem of disorder in the chain induced by particle positioning errors. We apply our methodology to a simplified model of a relevant physical platform, consisting of trapped ions. We are able to achieve extremely high excitation transfer in all cases, with different chain configurations and properties depending on the specific conditions.
引用
收藏
页数:13
相关论文
empty
未找到相关数据