2022/02/14 by Tangyou Huang, Tang-You Huang, Yue Ban +2
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Convolutional neural network #Fidelity #Gradient descent #Heuristic #Machine learning #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Quantum machine learning #Quantum many-body systems #Quantum mechanics #Task (project management) #Variety (cybernetics) #cond-mat.dis-nn #quant-ph
paper · pdf · doi:10.1103/physrevapplied.17.024040
published as Phys. Rev. Applied 17, 024040 (2022) · 14 pages,16 figures
openalex publication_date 2022/02/14 · arxiv created 2022/02/21 · arxiv updated 2022/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of the concept and analyze neural network-based machine learning algorithm for achieving feasible high-fidelity quantum control of a particle in random environment. To explicitly demonstrate its capabilities, we show that convolutional neural networks are able to solve this problem as they can recognize the disorder and, by supervised learning, further produce the policy for the efficient low-energy cost control of a quantum particle in a time-dependent random potential. We have shown that the accuracy of the proposed algorithm is enhanced by a higher-dimensional mapping of the disorder pattern and using two neural networks, each properly trained for the given task. The designed method, being computationally more efficient than the gradient-descent optimization, can be applicable to identify and control various noisy quantum systems on a heuristic basis.