When does reinforcement learning stand out in quantum control? A comparative study on state preparation

Zhang, Xiao‐Ming; Wei, Zezhu; Raza, Asad; Yang, Xu-Chen; Wang, Xin

doi:10.1038/s41534-019-0201-8

Cited by 122 publications

(93 citation statements)

References 51 publications

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“…However, the intensities of their pulses are nearly continuous, which may leave challenges to the experimental implementation. While the requirement of discrete pulses will inevitably reduce their performance [36]. In addition, their efficiency is limited by their iterative nature, which makes the task of designing pulses a big burden especially when there exist a large number of states waiting to be processed.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Wang

et al. 2021

EPJ Quantum Technol.

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Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. In this paper, we investigate how to prepare a certain single- or two-qubit target state from arbitrary initial states in semiconductor double quantum dots with only a few discrete control pulses by leveraging the deep reinforcement learning. Our method is based on the training of the network over numerous preparing tasks. The results show that once the network is well trained, it works for any initial states in the continuous Hilbert space. Thus repeated training for new preparation tasks is avoided. Our scheme outperforms the traditional optimization approaches based on gradient with both the higher efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against stochastic fluctuations within certain thresholds, such as the charge and nuclear noises.

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Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Wang

et al. 2021

EPJ Quantum Technol.

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“…Importantly, Ref. [226] benchmarks reinforcement learning for quantum control against traditional control methods for the problem of preparing a desired quantum state. More specifically, the authors compare the efficacy of three reinforcement learning algorithms, namely, tabular Q-learning, deep Q-learning, and policy gradient, with two traditional control methods: stochastic gradient descent and Krotov algorithms.…”

Section: Quantum Controlmentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

172

111

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Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.

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“…Niu et al (2019) used deep reinforcement learning and proposed a quantum control framework for fast and high-fidelity quantum gate control optimization. Zhang et al (2019) successfully used reinforcement learning algorithm to solve a class of quantum state control problems, and made a theoretical analysis.…”

Section: Introductionmentioning

confidence: 99%

A Quantum System Control Method Based on Enhanced Reinforcement Learning

Liu

Wang

Fan

et al. 2021

Preprint

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The design of quantum system control is a key task to a powerful quantum information technology. In practical, traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. So a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. A satisfactory control strategy is obtained through enhanced reinforcement learning so that the quantum system can be evolved accurately from the initial state to the target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL can achieve high fidelity learning control of quantum systems and improve the efficiency of quantum system control.

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When does reinforcement learning stand out in quantum control? A comparative study on state preparation

Cited by 122 publications

References 51 publications

Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Machine learning for quantum matter

A Quantum System Control Method Based on Enhanced Reinforcement Learning

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