Hybrid Quantum-Classical Dynamic Programming Algorithm

Chen, Chih-Chieh; Shiba, Kodai; Sogabe, Masaru; Sakamoto, Katsuyoshi; Sogabe, Tomah

doi:10.1007/978-3-030-73113-7_18

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Quantum algorithms are proven to have exponential or quadratic operational efficiency improvements in solving specific problems compared to classical algorithms 32,33 , such as integer factorization 34 and unstructured database searches 35 . Recent studies in variational quantum algorithms (VQA) have applied quantum computing to many scientific domains, including molecular dynamical studies 36 , quantum optimization 37,38 and various quantum machine learning (QML) applications such as regression [39][40][41] , classification 40,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] , generative modeling [57][58][59][60][61][62] , deep reinforcement learning [63][64][65][66][67][68][69] , sequence modeling 39,70,71 , speech identification 72 , distance metric learning 73,74 , transfer learning…”

Section: Quantum Architecture Search Via Truly Proximal Policy Optimi...mentioning

confidence: 99%

Quantum architecture search via truly proximal policy optimization

Zhu

Hou

2023

Sci Rep

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Quantum Architecture Search (QAS) is a process of voluntarily designing quantum circuit architectures using intelligent algorithms. Recently, Kuo et al. (Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021) proposed a deep reinforcement learning-based QAS (QAS-PPO) method, which used the Proximal Policy Optimization (PPO) algorithm to automatically generate the quantum circuit without any expert knowledge in physics. However, QAS-PPO can neither strictly limit the probability ratio between old and new policies nor enforce well-defined trust domain constraints, resulting in poor performance. In this paper, we present a new deep reinforcement learning-based QAS method, called Trust Region-based PPO with Rollback for QAS (QAS-TR-PPO-RB), to automatically build the quantum gates sequence from the density matrix only. Specifically, inspired by the research work of Wang, we employ an improved clipping function to implement the rollback behavior to limit the probability ratio between the new strategy and the old strategy. In addition, we use the triggering condition of the clipping based on the trust domain to optimize the policy by restricting the policy within the trust domain, which leads to guaranteed monotone improvement. Experiments on several multi-qubit circuits demonstrate that our presented method achieves better policy performance and lower algorithm running time than the original deep reinforcement learning-based QAS method.

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Section: Quantum Architecture Search Via Truly Proximal Policy Optimi...mentioning

confidence: 99%

Quantum architecture search via truly proximal policy optimization

Zhu

Hou

2023

Sci Rep

View full text Add to dashboard Cite

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“…Chen et al [18] proposed QRNN to address challenges like partial observability and long-term memory requirements in certain environments. Various methods have been developed to approximate the value function, including hybrid quantum-classical linear solvers [19]. Heimann et al [20] improved agent convergence by implementing Double DQN (DDQN) within the VQC framework.…”

Section: Related Workmentioning

confidence: 99%

Quantum Deep Q-Learning with Distributed Prioritized Experience Replay

Chen

2023

2023 IEEE International Conference on Quantum Computing and Engineering (QCE)

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This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.

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Hybrid Quantum-Classical Dynamic Programming Algorithm

Cited by 2 publications

References 9 publications

Quantum architecture search via truly proximal policy optimization

Quantum architecture search via truly proximal policy optimization

Quantum Deep Q-Learning with Distributed Prioritized Experience Replay

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