Experimentally Realizing Efficient Quantum Control with Reinforcement Learning

Ai, Ming-Zhong; Ding, Yongcheng; Ban, Yong-Ling; Martín-Guerrero, José D.; Casanova, Jorge; Cui, Jin-Ming; Huang, Yudong; Chen, Xi; Li, Chuan‐Feng; Guo, Guang‐Can

doi:10.48550/arxiv.2101.09020

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…In the past few years, deep reinforcement learning (DRL) has solved pulse design for fast and robust quantum state preparation [33]- [35], gate operation [36], and quantum Szilard engine [37]. However, as we highlighted in our previous researches [19], [20], we are still far from exploiting the power of RL for quantum control because of quantum measurement. RL requires the observation of states for outputting an action, conflicting with quantum mechanics' fundamental feature that the state is destroyed after direct quantum measurement.…”

Section: Introductionmentioning

confidence: 99%

Quantum Stream Learning

Ding¹,

Chen²,

Magdalena‐Benedito³

et al. 2021

Preprint

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The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.

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Section: Introductionmentioning

confidence: 99%

Quantum Stream Learning

Ding¹,

Chen²,

Magdalena‐Benedito³

et al. 2021

Preprint

Self Cite

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“…For now, physicists also complete quantum tasks, study properties of quantum systems, and design physics experiments with ML algorithms [3][4][5][6][7][8][9][10][11][12][13][14]. Its most utilized branch, so-called reinforcement learning (RL) [15], has naturally shown its capability in quantum control [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. It is also related to quantum information retrieval by controlling the measurement process [32,33], which has been extended to a quantum version [34] for optimal measurement control [35,36].…”

Section: Introductionmentioning

confidence: 99%

Active Learning for the Optimal Design of Multinomial Classification in Physics

Ding¹,

Martín-Guerrero²,

Song³

et al. 2021

Preprint

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Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze its capability as an assistant for the design of experiments, extracting maximum information for learning with the minimal cost in fidelity loss, or reducing total operation costs of labeling in the laboratory. We present two typical applications as quantum information retrieval in qutrits and phase boundary prediction in many-body physics. For an equivalent multinomial classification problem, we achieve the correct rate of 99% with less than 2% samples labeled. We reckon that active-learning-inspired physics experiments will remarkably save budget without loss of accuracy.

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“…For trapped ion systems, Ref. [36] demonstrates application of deep reinforcement learning to robust single-qubit gate.…”

Section: Introductionmentioning

confidence: 99%

Batch Optimization of Frequency-Modulated Pulses for Robust Two-qubit Gates in Ion Chains

Kang,

Liang,

Zhang

et al. 2021

Preprint

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Two-qubit gates in trapped ion quantum computers are generated by applying spin-dependent forces that temporarily entangle the internal state of the ion with its motion. Laser pulses are carefully designed to generate a maximally entangling gate between the ions while minimizing any residual entanglement between the motion and the ion. The quality of the gates suffers when actual experimental parameters differ from the ideal case. Here we improve the robustness of frequencymodulated Mølmer-Sørensen gates to motional mode frequency offsets by optimizing average performance over a range of systematic errors using batch optimization. We then compare this method to frequency modulated gates optimized for ideal parameters that include an analytic robustness condition. Numerical simulations show good performance up to 12 ions and the method is experimentally demonstrated on a two-ion chain.

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Experimentally Realizing Efficient Quantum Control with Reinforcement Learning

Cited by 4 publications

References 47 publications

Quantum Stream Learning

Quantum Stream Learning

Active Learning for the Optimal Design of Multinomial Classification in Physics

Batch Optimization of Frequency-Modulated Pulses for Robust Two-qubit Gates in Ion Chains

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