Classifying global state preparation via deep reinforcement learning

Haug, Tobias; Mok, Wai-Keong; You, Jinhong; Zhang, Wenzu; Png, Ching Eng; Kwek, Leong‐Chuan

doi:10.1088/2632-2153/abc81f

Cited by 42 publications

(24 citation statements)

References 26 publications

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“…We point out that by combining our scheme with Ref. [53], the driving between arbitrary states can be realized.…”

Section: Introductionmentioning

confidence: 90%

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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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“…We point out that by combining our scheme with Ref. [53], the driving between arbitrary states can be realized.…”

Section: Introductionmentioning

confidence: 90%

“…Recently, the generation of arbitrary states from a specific state [53] in nitrogen-vacancy center has been realized with the aid of the deep RL. Then it is intriguing to check if the deep RL can be used to realize a contrary problem: preparing a certain target state from arbitrary initial states, i.e., universal state preparation (USP).…”

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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“…It is also natural to consider applying reinforcement learning (RL) individually for quantum control tasks [21]- [32]. 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.…”

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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“…More recently machine learning techniques have emerged as a viable option for finding alternative optimal control schemes. In particular reinforcement learning (RL) has been employed in the context of state preparation [29,30], circuit architecture design [31] and control of multi-level systems [32]. In the context of three level systems, deep neural network based RL has been used along with state monitoring to learn optimal pulse shapes for driving fields [33,34].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems

Brown,

Sgroi,

Giannelli

et al. 2021

Preprint

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We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer in a multi-level system. We constraint our strategy to the case of fixed coupling rates but time-varying detunings, a situation that would simplify considerably the implementation of population transfer in relevant experimental platforms, such as semiconducting and superconducting ones. Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols that, remarkably, differ from (and can be superior to) standard Raman, STIRAP or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.

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Classifying global state preparation via deep reinforcement learning

Cited by 42 publications

References 26 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

Quantum Stream Learning

Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems

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