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Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning.
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“Quantum Algorithm for Systems of Linear Equations with Exponentially Improved Dependence on Precision”
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“A Markovian decision process”
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“The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines”
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“Simulating physics with computers”
Richard. Feynman · 1982
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“Multilayer feedforward networks are universal approximators”
Kurt Hornik, Maxwell Stinchcombe and Halbert White · 1989
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“Rapid Solution of Problems by Quantum Computation”
D. Deutsch and R. Jozsa · 1992
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“Self-improving reactive agents based on reinforcement learning, planning and teaching”
Long-Ji Lin · 1992
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“Q-learning”
Christopher… Watkins and Peter Dayan · 1992
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“Simple statistical gradient-following algorithms for connectionist reinforcement learning”
Ronald. Williams · 1992
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“On-line Q Q -learning using connectionist systems”
Gavin Rummery and Mahesan Niranjan · 1994
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“A quantum algorithm for finding the minimum”
Christoph Durr and Peter Hoyer · 1996
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“Value iteration and policy iteration algorithms for Markov decision problem”
Elena Pashenkova, Irina Rish and Rina Dechter · 1996
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“Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer”
Peter. Shor · 1997
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“Natural Gradient Works Efficiently in Learning”
Shun-Ichi Amari · 1998
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“Policy gradient methods for reinforcement learning with function approximation”
Richard. Sutton, David McAllester, Satinder Singh and Yishay Mansour · 1999
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“Quantum amplitude amplification and estimation”
Gilles Brassard, Peter Hoyer, Michele Mosca and Alain Tapp · 2002
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“Quantum computing with spin qubits in semiconductor structures”
Vladimir Privman, Dima Mozyrsky and Israel Vagner · 2002
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“On Actor-Critic Algorithms”
Vijay Konda and John. Tsitsiklis · 2003
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“Least-squares policy iteration”
Michail Lagoudakis and Ronald Parr · 2003
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“Tree-based batch mode reinforcement learning”
Damien Ernst, Pierre Geurts and Louis Wehenkel · 2005
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“Fast Quantum Algorithm for Numerical Gradient Estimation”
Stephen. Jordan · 2005
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“Offline reinforcement learning: Tutorial, review, and perspectives on open problems”
Sergey Levine, Aviral Kumar, George Tucker and Justin Fu · 2005
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“Quantum search in stochastic planning”
Sanjeev Naguleswaran and Langford. White · 2005
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“Quantum computation for action selection using reinforcement learning”
Chun-Lin Chen, Daoyi Dong and Zonghai Chen · 2006
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“A quantum reinforcement learning method for repeated game theory”
Chunlin Chen, Daoyi Dong, Yu Dong and Qiong Shi · 2006
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“Quantum mechanics helps in learning for more intelligent robots”
Daoyi Dong, Chun-Lin Chen, Zonghai Chen and Chen-Bin Zhang · 2006
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“Quantum robot: structure, algorithms and applications”
Daoyi Dong, Chunlin Chen, Chenbin Zhang and Zonghai Chen · 2006
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“Complex-valued reinforcement learning”
Tomoki Hamagami, Takashi Shibuya and Shingo Shimada · 2006
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“Matrix product state representations”
David Perez-Garcia, Frank Verstraete, Michael Wolf and J Cirac · 2006
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“Superconducting Circuits and Quantum Information”
J.. You and Franco Nori · 2006
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“Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning”
Chun-Lin Chen and Daoyi Dong · 2008
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“Incoherent control of quantum systems with wavefunction-controllable subspaces via quantum reinforcement learning”
Daoyi Dong et al · 2008
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“Quantum reinforcement learning”
Daoyi Dong, Chun-Lin Chen, Han-Xiong Li and Tzyh-Jong Tarn · 2008
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“Maximum entropy inverse reinforcement learning”
Brian. Ziebart, Andrew. Maas, J. Bagnell and Anind. Dey · 2008
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“Reservoir computing approaches to recurrent neural network training”
Mantas Lukoševičius and Herbert Jaeger · 2009
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“Complexity analysis of quantum reinforcement learning”
Chunlin Chen and Daoyi Dong · 2010
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Ran Cheng · 2010
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“Robust Quantum-Inspired Reinforcement Learning for Robot Navigation”
Daoyi Dong, Chun-Lin Chen, Jian Chu and Tzyh-Jong Tarn · 2010
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“Understanding the difficulty of training deep feedforward neural networks”
Xavier Glorot and Yoshua Bengio · 2010
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“Double Q-learning”
Hado Hasselt · 2010
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“Near-optimal Regret Bounds for Reinforcement Learning”
Thomas Jaksch, Ronald Ortner and Peter Auer · 2010
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“Quantum information with Rydberg atoms”
Mark Saffman, Thad Walker and Klaus Mølmer · 2010
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“Analysis and Improvement of Policy Gradient Estimation”
Tingting Zhao, Hirotaka Hachiya, Gang Niu and Masashi Sugiyama · 2011
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“Projective simulation for artificial intelligence”
Hans. Briegel and Gemma De · 2012
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“Hybrid control of uncertain quantum systems via fuzzy estimation and quantum reinforcement learning”
Chen Chunlin, Jiang Frank and Dong Daoyi · 2012
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“Quantum reinforcement learning in continuous action space”
Shaojun Wu, Shan Jin, Dingding Wen and Xiaoting Wang · 2012
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“Quantum inspired reinforcement learning in changing environment”
Pegah Fakhari, Karthikeyan Rajagopal, SN Balakrishnan and JR Busemeyer · 2013
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“Quantum partially observable Markov decision processes”
Jennifer Barry, Daniel. Barry and Scott Aaronson · 2014
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“Quantum Speedup for Active Learning Agents”
Giuseppe Paparo et al · 2014
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“Quantum-enhanced deliberation of learning agents using trapped ions”
Vedran Dunjko, Nicolai Friis and Hans Briegel · 2015
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“Framework for learning agents in quantum environments”
Vedran Dunjko, Jacob Taylor and Hans Briegel · 2015
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“Human-level control through deep reinforcement learning”
Volodymyr Mnih et al · 2015
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“Quantum speedup of Monte Carlo methods”
Ashley Montanaro · 2015
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“A quantum inspired reinforcement learning technique for beyond next generation wireless networks”
Sinan Nuuman, David Grace and Tim Clarke · 2015
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“Artificial Life in Quantum Technologies”
Unai Alvarez-Rodriguez, Mikel Sanz, Lucas Lamata and Enrique Solano · 2016
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“Quantum-Enhanced Machine Learning”
Vedran Dunjko, Jacob. Taylor and Hans. Briegel · 2016
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“Quantum Computation and Quantum Information (10th Anniversary edition)”
M.. Nielsen and Chuang. L · 2016
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“Deep reinforcement learning with double q-learning”
Hado Van, Arthur Guez and David Silver · 2016
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“Minimax Regret Bounds for Reinforcement Learning”
Mohammad Azar, Ian Osband and Rémi Munos · 2017
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“Deep reinforcement learning: A brief survey”
Kai Arulkumaran, Marc Deisenroth, Miles Brundage and Anil Bharath · 2017
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“Advances in quantum reinforcement learning”
Vedran Dunjko, Jacob Taylor and Hans Briegel · 2017
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“Reinforcement learning with deep energy-based policies”
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel and Sergey Levine · 2017
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“Basic protocols in quantum reinforcement learning with superconducting circuits”
Lucas Lamata · 2017
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“Free energy-based reinforcement learning using a quantum processor”
Anna Levit et al · 2017
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“Multi-agent actor-critic for mixed cooperative-competitive environments”
Ryan Lowe et al · 2017
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“Projective simulation with generalization”
Alexey. Melnikov, Adi Makmal, Vedran Dunjko and Hans. Briegel · 2017
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“A complex-valued reinforcement learning method using complex-valued neural networks”
Masaki Mochida, Hidehiro Nakano and Arata Miyauchi · 2017
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“Traffic flow optimization using a quantum annealer”
Florian Neukart et al · 2017
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“Optimization by a quantum reinforcement algorithm”
A Ramezanpour · 2017
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“Measurement-based adaptation protocol with quantum reinforcement learning”
Francisco Albarrán-Arriagada, Juan Retamal, Enrique Solano and Lucas Lamata · 2018
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“Quantum Artificial Life in an IBM Quantum Computer”
Unai Alvarez-Rodriguez, Mikel Sanz, Lucas Lamata and Enrique Solano · 2018
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“Multiqubit and multilevel quantum reinforcement learning with quantum technologies”
Francisco Cárdenas-López, Lucas Lamata, Juan Retamal and Enrique Solano · 2018
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“Quantum gradient estimation and its application to quantum reinforcement learning”, 2018
Arjan Cornelissen · 2018
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“Reinforcement Learning Using Quantum Boltzmann Machines”
Daniel Crawford et al · 2018
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“Exponential improvements for quantum-accessible reinforcement learning”
Vedran Dunjko, Yi-Kai Liu, Xingyao Wu and Jacob Taylor · 2018
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“Reinforcement Learning with Neural Networks for Quantum Feedback”
Thomas Fösel, Petru Tighineanu, Talitha Weiss and Florian Marquardt · 2018
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“Soft actor-critic algorithms and applications”
Tuomas Haarnoja et al · 2018
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“Attention, learn to solve routing problems!”
Wouter Kool, Herke Van and Max Welling · 2018
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“Reinforcement Learning: An Introduction”
R.. Sutton and A.. Barto · 2018
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“A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play”
David Silver et al · 2018
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“Supervised Learning with Quantum Computers”
Maria Schuld and Francesco Petruccione · 2018
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“Quantum Speedups for Exponential-Time Dynamic Programming Algorithms”
Andris Ambainis et al · 2019
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“Quantum Supremacy using a Programmable Superconducting Processor”
Frank Arute et al · 2019
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“Quantum convolutional neural networks”
Iris Cong, Soonwon Choi and Mikhail. Lukin · 2019
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“The Power of Block-Encoded Matrix Powers: Improved Regression Techniques via Faster Hamiltonian Simulation”
Shantanav Chakraborty, András Gilyén and Stacey Jeffery · 2019
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“Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines”
Chih-Chieh Chen, Shiue-Yuan Shiau, Ming-Feng Wu and Yuh-Renn Wu · 2019
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“Optimizing quantum optimization algorithms via faster quantum gradient computation”
“Unentangled quantum reinforcement learning agents in the OpenAI Gym”
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András Gilyén, Srinivasan Arunachalam and Nathan Wiebe · 2019
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“Quantum Multiple Q-Learning”
Michael Ganger and Wei Hu · 2019
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“Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics”
András Gilyén, Yuan Su, Guang Low and Nathan Wiebe · 2019
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“An initialization strategy for addressing barren plateaus in parametrized quantum circuits”
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski and Marcello Benedetti · 2019
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“ Q Q Learning with Quantum Neural Networks”
Wei Hu and James Hu · 2019
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“Distributional Reinforcement Learning with Quantum Neural Networks”
Wei Hu and James Hu · 2019
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“Reinforcement Learning with Deep Quantum Neural Networks”
Wei Hu and James Hu · 2019
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