Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics

NEUROCOMPUTING

卷:268

Article

Palittapongarnpim, Pantita¹  Wittek, Peter^2,3  Zahedinejad, Ehsan¹  Vedaie, Shakib¹  Sanders, Barry C.^1,4,5,6,7 

[1] Univ Calgary, Inst Quantum Sci & Technol, Calgary, AB T2N 1N4, Canada

[2] ICFO Inst Photon Sci, Mediterranean Technol Pk Av Carl Friedrich Gauss, Barcelona 08860, Spain

[3] Univ Boras, SE-50190 Boras, Sweden

[4] Canadian Inst Adv Res, Program Quantum Informat Sci, Toronto, ON M5G 1Z8, Canada

[5] Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Hefei 230026, Anhui, Peoples R China

[6] Univ Sci & Technol China, Shanghai Branch, CAS Ctr Excellence, Shanghai 201315, Peoples R China

[7] Univ Sci & Technol China, Shanghai Branch, Synerget Innovat Ctr Quantum Informat & Quantum P, Shanghai 201315, Peoples R China

关键词: Machine learning;    Reinforcement learning;    Quantum control;    Differential evolution;    Feedback control;    Quantum physics;   

DOI  :  10.1016/j.neucom.2016.12.087

来源: Elsevier

【 摘 要 】

Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical systems, quantum control for parameter optimization is mainly pursued via gradient based greedy algorithms. Although the quantum fitness landscape is often compatible with greedy algorithms, sometimes greedy algorithms yield poor results, especially for large-dimensional quantum systems. We employ differential evolution algorithms to circumvent the stagnation problem of non-convex optimization. We improve quantum control fidelity for noisy system by averaging over the objective function. To reduce computational cost, we introduce heuristics for early termination of runs and for adaptive selection of search subspaces. Our implementation is massively parallel and vectorized to reduce run time even further. We demonstrate our methods with two examples, namely quantum phase estimation and quantum-gate design, for which we achieve superior fidelity and scalability than obtained using greedy algorithms. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free