Quantum model learning agent: characterisation of quantum systems through machine learning
Quantum model learning agent: characterisation of quantum systems through machine learning
复制标题
量子模型学习代理：通过机器学习表征量子系统
DOI：
10.1088/1367-2630/ac68ff
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发表时间：
2022
影响因子：
3.3
通讯作者：
Flynn B
中科院分区：
文献类型：
--
作者：
Flynn B
真实的量子系统的精确模型对于研究它们的行为很重要，但很难凭经验进行验证。在这里，我们报告一个算法-量子模型学习代理（QMLA）-逆向工程哈密顿描述的目标系统。我们测试的性能QMLA的一些模拟实验，展示了几种机制的候选哈密顿模型的设计，同时娱乐的物理相互作用的性质，管理系统的研究中的许多假设。在大多数情况下，当提供有限的先验信息和实验设置的控制时，QMLA被证明可以识别真实的模型。我们的协议可以探索伊辛，海森堡和哈伯德家庭的模型并行，可靠地确定家庭最好的描述系统动力学。我们证明QMLA操作大型模型空间，通过将遗传算法制定新的假设模型。其特征传播到下一代的模型的选择是基于受Elo评级方案启发的目标函数，该方案通常用于对国际象棋和足球等游戏中的竞争对手进行评级。在所有情况下，我们的协议发现，与真实模型相比，F1得分为0.88的模型，并且在72%的情况下精确地识别出真实模型，同时探索了超过25万个潜在模型的空间。通过测试目标系统中实际发生的相互作用，QMLA是探索基础物理和量子器件表征和校准的可行工具。
Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.
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DOI：
10.1103/prxquantum.3.030345
发表时间：
2021-09
期刊：
PRX quantum : a Physical Review journal
影响因子：
--
作者：
T. O’Brien;L. Ioffe;Yuan Su;D. Fushman;H. Neven;R. Babbush;V. Smelyanskiy
通讯作者：
T. O’Brien;L. Ioffe;Yuan Su;D. Fushman;H. Neven;R. Babbush;V. Smelyanskiy
影响因子：
13.6
作者：
Lokhov AY;Vuffray M;Misra S;Chertkov M
通讯作者：
Chertkov M
影响因子：
2.9
作者：
Agnes Valenti;Guliuxin Jin;J. L'eonard;S. Huber;E. Greplova
通讯作者：
Agnes Valenti;Guliuxin Jin;J. L'eonard;S. Huber;E. Greplova
影响因子：
7.9
作者：
Hvattum, Lars Magnus;Arntzen, Halvard
通讯作者：
Arntzen, Halvard
DOI：
10.1109/isvlsi49217.2020.00034
发表时间：
2020
期刊：
2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
影响因子：
--
作者：
T. Ayral;François;Zain Saleem;Y. Alexeev;Martin Suchara
通讯作者：
Martin Suchara