Quantum machine learning with glow for episodic tasks and decision games

Clausen, J.; Briegel, Hans J.

doi:10.1103/physreva.97.022303

Cited by 12 publications

(10 citation statements)

References 36 publications

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“…In the domain of quantum machine learning, RL has received relatively less attention, considering the quantum enhancements in supervised and unsupervised learning [6][7][8][9][10][11][12][13][14][15][16][17][18]. In our previous work [19], we solved the contextual bandit problem with a quantum neural network.…”

Section: Introductionmentioning

confidence: 99%

Q Learning with Quantum Neural Networks

Hu¹,

Hu²

2019

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Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.

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Section: Introductionmentioning

confidence: 99%

Q Learning with Quantum Neural Networks

Hu¹,

Hu²

2019

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“…To attack the issue, on the other hand, we proposed to employ the machin-ery that plays (or simulates) the decision processes made by the rational players. We hope that the present work would accelerate the studies on potential applications, including quantum cryptography [24,25] and quantum machine learning [26].…”

Section: Discussionmentioning

confidence: 86%

Experimental Demonstration on Quantum Sensitivity to Available Information in Decision Making

Lee

Bang

Lee

et al. 2019

Sci Rep

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We present an experimental illustration on the quantum sensitivity of decision making machinery. In the decision making process, we consider the role of available information, say hint, whether it influences the optimal choices. To the end, we consider a machinery method of decision making in a probabilistic way. Our main result shows that in decision making process our quantum machine is more highly sensitive than its classical counterpart to the hints we categorize into “good” and “poor”. This quantum feature originates from the quantum superposition involved in the decision making process. We also show that the quantum sensitivity persists before the quantum superposition is completely destroyed.

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“…In the latter work, the authors also provide a concise yet extensive overview of related topics, and outline a perspective which unifies various aspects of ML and RL in an approach to resolve hard quantum measurement and control problems. In (Clausen and Briegel, 2016), RL based on PS updates was analyzed in the context of general control-and-feedback problems. Finally, ideas of unified computational platforms for quantum control, albeit without explicit emphasis on ML techniques had been previously provided in (Machnes et al, 2011).…”

Section: On-line Designmentioning

confidence: 99%

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

2018

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Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.

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Quantum machine learning with glow for episodic tasks and decision games

Cited by 12 publications

References 36 publications

Q Learning with Quantum Neural Networks

Q Learning with Quantum Neural Networks

Experimental Demonstration on Quantum Sensitivity to Available Information in Decision Making

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

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