Approximation of quantum control correction scheme using deep neural networks

被引：16
|
作者：
Ostaszewski, M. [1 ]
Miszczak, J. A. [1 ]
Banchi, L. [2 ]
Sadowski, P. [1 ]
机构：
[1] Polish Acad Sci, Inst Theoret & Appl Informat, Baltycka 5, PL-44100 Gliwice, Poland
[2] Imperial Coll London, Blackett Lab, QOLS, London SW7 2AZ, England
来源：
QUANTUM INFORMATION PROCESSING | 2019年 / 18卷 / 05期
基金：
英国工程与自然科学研究理事会;
关键词：
Quantum dynamics; Quantum control; Deep learning; Recurrent neural network; !text type='PYTHON']PYTHON[!/text] FRAMEWORK; DYNAMICS; QUTIP;
D O I：
10.1007/s11128-019-2240-7
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
We study the functional relationship between quantum control pulses in the idealized case and the pulses in the presence of an unwanted drift. We show that a class of artificial neural networks called LSTM is able to model this functional relationship with high efficiency, and hence the correction scheme required to counterbalance the effect of the drift. Our solution allows studying the mapping from quantum control pulses to system dynamics and analysing its behaviour with respect to the local variations in the control profile.
引用
收藏
页数：13
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