Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

Kwak, Yunseok; Yun, Won Joon; Jung, Soyi; Kim, Jong-Kook

doi:10.1109/ictc52510.2021.9620885

Cited by 35 publications

(20 citation statements)

References 9 publications

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“…This should be doable, as one knows the state preparation routine Summary. The paper by Kwak et al [Kwa+21] gives a short introduction to both RL and (variational) QC. This is followed up by a tutorial on how to implement a VQC-enhanced RL algorithm with PennyLane to solve the CartPole environment.…”

Section: Algorithmicmentioning

confidence: 99%

A Survey on Quantum Reinforcement Learning

Meyer¹,

Ufrecht²,

Periyasamy³

et al. 2022

Preprint

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Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning (our interpretation of this term will be clarified below), we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.

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Section: Algorithmicmentioning

confidence: 99%

A Survey on Quantum Reinforcement Learning

Meyer¹,

Ufrecht²,

Periyasamy³

et al. 2022

Preprint

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“…3) Control: Quantum reinforcement learning (QRL) has been actively studied. [53], [54] have proposed the hybrid computing methods, i.e., the controller policy is based on VQC, and the evaluator-side network (i.e., critic) is based on a classical neural network. In addition, [55], [56] have proposed an utterly quantum version of the reinforcement learning regime.…”

Section: Autonomous Mobilitymentioning

confidence: 99%

Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

Kwak¹,

Yun²,

Kim³

et al. 2022

Preprint

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Deep learning (DL) has already become a state-ofthe-art technology for various data processing tasks. However, data security and computational overload problems frequently occur due to their high data and computational power dependence. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) are emerging to complement existing DL methods by reducing computational overhead and strengthening data security. Furthermore, a quantum distributed deep learning (QDDL) technique that combines these advantages and maximizes them is in the spotlight. QDL takes computational gains by replacing deep learning computations on local devices and servers with quantum deep learning. On the other hand, besides the advantages of the existing distributed learning structure, it can increase data security by using a quantum secure communication protocol between the server and the client. Although many attempts have been made to confirm and demonstrate these various possibilities, QDDL research is still in its infancy. This paper discusses the model structure studied so far and its possibilities and limitations to introduce and promote these studies. It also discusses the areas of applied research so far and in the future and the possibilities of new methodologies.

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“…Some works use classical-quantum hybrid model to solve large problems [23,24]. Other works use variational quantum circuit to represent policy in reinforcement learning [25,26]. Variational quantum circuit is also applied to represent both policy and value function in actor-critic method [27].…”

Section: Introductionmentioning

confidence: 99%

Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

Kimura

Shiba

Chen³

et al. 2021

Mathematical Problems in Engineering

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Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

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Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

Cited by 35 publications

References 9 publications

A Survey on Quantum Reinforcement Learning

A Survey on Quantum Reinforcement Learning

Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

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