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Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing.
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Nielsen, M. A.; and Chuang, I. 2002 · 2002
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Quantum process tomography of a controlled-NOT gate
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A Concise Introduction to Decentralized POMDPs
Oliehoek, F. A.; and Amato, C. 2016 · 2016
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Multiagent cooperation and competition with deep reinforcement learning
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Farquhar, S.; and Gal, Y. 2018 · 2018
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Quantum circuit learning
Mitarai, K.; Negoro, M.; Kitagawa, M.; and Fujii, K. 2018 · 2018
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Supervised learning with quantum-enhanced feature spaces
Havlíček, V.; Córcoles, A. D.; Temme, K.; Harrow, A. W.; Kandala, A.; Chow, J. M.; and Gambetta, J. M. 2019 · 2019
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Continuous-variable quantum neural networks
Killoran, N.; Bromley, T. R.; Arrazola, J. M.; Schuld, M.; Quesada, N.; and Lloyd, S. 2019 · 2019
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Evaluating analytic gradients on quantum hardware
Schuld, M.; Bergholm, V.; Gogolin, C.; Izaac, J.; and Killoran, N. 2019 · 2019
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Quantum machine learning in feature Hilbert spaces
Schuld, M.; and Killoran, N. 2019 · 2019
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QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Son, K.; Kim, D.; Kang, W. J.; Hostallero, D.; and Yi, Y. 2019 · 2019
Circuit-centric quantum classifiers
Schuld, M.; Bocharov, A.; Svore, K. M.; and Wiebe, N. 2020 · 2020
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Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
Harrow, A. W.; and Napp, J. C. 2021 · 2021
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Variational quantum policies for reinforcement learning
Jerbi, S.; Gyurik, C.; Marshall, S.; Briegel, H. J.; and Dunjko, V. 2021 · 2021
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Optimizing quantum heuristics with meta-learning
Wilson, M.; Stromswold, R.; Wudarski, F.; Hadfield, S.; Tubman, N. M.; and Rieffel, E. G. 2021 · 2021
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Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
Daniels, Z.; Raghavan, A.; Hostetler, J.; Rahman, A.; Sur, I.; Piacentino, M.; and Divakaran, A. 2022 · 2022
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Cited alongside, same era.
Variational quantum circuits for deep reinforcement learning
Chen, S. Y.-C.; Yang, C.-H. H.; Qi, J.; Chen, P.-Y.; Ma, X.; and Goan, H.-S. 2020 · 2020
Cited alongside, same era.
Noise-resilient variational hybrid quantum-classical optimization
Gentini, L.; Cuccoli, A.; Pirandola, S.; Verrucchi, P.; and Banchi, L. 2020 · 2020
Cited alongside, same era.
Understanding the role of training regimes in continual learning
Mirzadeh, S. I.; Farajtabar, M.; Pascanu, R.; and Ghasemzadeh, H. 2020 · 2020
Cited alongside, same era.
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Rashid, T.; Samvelyan, M.; de Witt, C. S.; Farquhar, G.; Foerster, J. N.; and Whiteson, S. 2020 · 2020
Cited alongside, same era.
Playing atari with hybrid quantum-classical reinforcement learning
Lockwood, O.; and Si, M. 2020a
Cited in the paper.
Reinforcement learning with quantum variational circuit
Lockwood, O.; and Si, M. 2020b
Cited in the paper.
Gambetta, J. 2022 · 2022
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Is Quantum Advantage the Right Goal for Quantum Machine Learning?
Schuld, M.; and Killoran, N. 2022 · 2022
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Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
Yun, W. J.; Kwak, Y.; Kim, J. P.; Cho, H.; Jung, S.; Park, J.; and Kim, J. 2022 · 2022
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IBM promises 1000-qubit quantum computer—a milestone—by 2023
Cho, A. 2020 · 2023
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Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward
Sunehag, P.; Lever, G.; Gruslys, A.; Czarnecki, W. M.; Zambaldi, V. F.; Jaderberg, M.; Lanctot, M.; Sonnerat, N.; Leibo, J. Z.; Tuyls, K.; and Graepel, T. 2018 · 2087
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