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The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently.
Optimizing qaoa: Success probability and runtime dependence on circuit depth
Murphy Yuezhen Niu, Sirui Lu, and Isaac L Chuang · 1905
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Optimal quantum control with poor statistics
Frederic Sauvage and Florian Mintert · 1909
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An efficient method for finding the minimum of a function of several variables without calculating derivatives
Michael JD Powell · 1964
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Control of photochemical branching: Novel procedures for finding optimal pulses and global upper bounds
David J Tannor, Vladimir Kazakov, and Vladimir Orlov · 1992
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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J Williams · 1992
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Almost any quantum logic gate is universal
Seth Lloyd · 1995
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Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier · 2001
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Particle swarm optimization: developments, applications and resources
Yuhui Shi et al · 2001
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A natural policy gradient
Sham M Kakade · 2002
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A differentiable programming method for quantum control
Frank Schäfer, Michal Kloc, Christoph Bruder, and Niels Lörch · 2002
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Natural evolution strategies and quantum approximate optimization
Tianchen Zhao, Giuseppe Carleo, James Stokes, and Shravan Veerapaneni · 2002
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Variance reduction techniques for gradient estimates in reinforcement learning
Evan Greensmith, Peter L Bartlett, and Jonathan Baxter · 2004
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
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Implementing the nelder-mead simplex algorithm with adaptive parameters
Fuchang Gao and Lixing Han · 2012
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Qutip: An open-source python framework for the dynamics of open quantum systems
J Robert Johansson, PD Nation, and Franco Nori · 2012
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Fidelity-based probabilistic q-learning for control of quantum systems
Chunlin Chen, Daoyi Dong, Han-Xiong Li, Jian Chu, and Tzyh-Jong Tarn · 2013
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Qutip 2: A python framework for the dynamics of open quantum systems
J Robert Johansson, Paul D Nation, and Franco Nori · 2013
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Robust control of quantum gates via sequential convex programming
Robert L Kosut, Matthew D Grace, and Constantin Brif · 2013
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A Quantum Approximate Optimization Algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien · 2014
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Deep reinforcement learning for quantum gate control
Zheng An and DL Zhou · 2019
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A view of estimation of distribution algorithms through the lens of expectation-maximization
David H Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, and Jennifer Listgarten · 2019
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Manipulation of spin dynamics by deep reinforcement learning agent
Jun-Jie Chen and Ming Xue · 2019
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Global optimization of quantum dynamics with alphazero deep exploration
Mogens Dalgaard, Felix Motzoi, Jens Jakob Sorensen, and Jacob Sherson · 2019
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Glassy phase of optimal quantum control
Alexandre GR Day, Marin Bukov, Phillip Weinberg, Pankaj Mehta, and Dries Sels · 2019
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Joshua V Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, and Rif A Saurous · 2017
Cited alongside, same era.
Quantum autoencoders for efficient compression of quantum data
Jonathan Romero, Jonathan P Olson, and Alan Aspuru-Guzik · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Quspin: a python package for dynamics and exact diagonalisation of quantum many body systems part i: spin chains
Phillip Weinberg and Marin Bukov · 2017
Cited alongside, same era.
Optimizing variational quantum algorithms using pontryagin’s minimum principle
Zhi-Cheng Yang, Armin Rahmani, Alireza Shabani, Hartmut Neven, and Claudio Chamon · 2017
Cited alongside, same era.
Taking gradients through experiments: Lstms and memory proximal policy optimization for black-box quantum control
Moritz August and José Miguel Hernández-Lobato · 2018
Cited alongside, same era.
Later among the works it cites.
Robust control optimization for quantum approximate optimization algorithm
Yulong Dong, Xiang Meng, Lin Lin, Robert Kosut, and K Birgitta Whaley · 2019
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From the quantum approximate optimization algorithm to a quantum alternating operator ansatz
Stuart Hadfield, Zhihui Wang, Bryan O’Gorman, Eleanor G Rieffel, Davide Venturelli, and Rupak Biswas · 2019
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Efficient variational simulation of non-trivial quantum states
Wen Wei Ho and Timothy H Hsieh · 2019
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Channel decoding with quantum approximate optimization algorithm
Toshiki Matsumine, Toshiaki Koike-Akino, and Ye Wang · 2019
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On the universality of the quantum approximate optimization algorithm
Mauro ES Morales, Jacob Biamonte, and Zoltán Zimborás · 2019
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Sequential minimal optimization for quantum-classical hybrid algorithms
Ken M Nakanishi, Keisuke Fujii, and Synge Todo · 2019
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Coherent transport of quantum states by deep reinforcement learning
Riccardo Porotti, Dario Tamascelli, Marcello Restelli, and Enrico Prati · 2019
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Deep reinforcement learning for robust quantum optimization
Vegard B Sørdal and Joakim Bergli · 2019
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Training the quantum approximate optimization algorithm without access to a quantum processing unit
Michael Streif and Martin Leib · 2019
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A quantum approximate optimization algorithm for continuous problems
Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler, and Nathan Killoran · 2019
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Quspin: a python package for dynamics and exact diagonalisation of quantum many body systems. part ii: bosons, fermions and higher spins
Phillip Weinberg and Marin Bukov · 2019
Later among the works it cites.
Learning robust and high-precision quantum controls
Re-Bing Wu, Haijin Ding, Daoyi Dong, and Xiaoting Wang · 2019
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When reinforcement learning stands out in quantum control? a comparative study on state preparation
Xiao-Ming Zhang, Zezhu Wei, Raza Asad, Xu-Chen Yang, and Xin Wang · 2019
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Reinforcement learning assisted quantum optimization
Matteo M Wauters, Emanuele Panizon, Glen B Mbeng, and Giuseppe E Santoro · 2020
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