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We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms.
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2009
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Kun Cao, Zheng-Wei Zhou, Guang-Can Guo, and Lixin He, “Efficient numerical method to calculate the three-tangle of mixed states,” Phys. Rev. A 81
2010
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2011
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Bastian Jungnitsch, Tobias Moroder, and Otfried Gühne, “Entanglement witnesses for graph states: General theory and examples,” Phys, Rev. A 84
2011
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A Chiuri, C Greganti, M Paternostro, G Vallone, and P Mataloni, “Experimental quantum networking protocols via four-qubit hyperentangled Dicke states,” Phys. Rev. Lett. 109
2012
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Pantita Palittapongarnpim, Peter Wittek, Ehsan Zahedinejad, Shakib Vedaie, and Barry C Sanders, “Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics,” Neurocomputing 268
2017
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2017
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2017
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Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction , 2nd ed. (The MIT Press, 2018)
2018
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2012
Cited alongside, same era.
Antonio D Córcoles, Jay M Gambetta, Jerry M Chow, John A Smolin, Matthew Ware, Joel Strand, Britton LT Plourde, and Matthias Steffen, “Process verification of two-qubit quantum gates by randomized benchmarking,” Phys. Rev. A 87
2013
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Joydip Ghosh, Andrei Galiautdinov, Zhongyuan Zhou, Alexander N Korotkov, John M Martinis, and Michael R Geller, “High-fidelity controlled- σ \sigma z gate for resonator-based superconducting quantum computers,” Physical Review A 87
2013
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2013
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Iulia M Georgescu, Sahel Ashhab, and Franco Nori, “Quantum simulation,” Rev. Mod. Phys. 86
2014
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2014
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2014
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2014
Cited alongside, same era.
Raymond J Spiteri, Marina Schmidt, Joydip Ghosh, Ehsan Zahedinejad, and Barry C Sanders, “Quantum control for high-fidelity multi-qubit gates,” New Journal of Physics 20
2018
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Shaowei Li, Anthony D. Castellano, Shiyu Wang, Yulin Wu, Ming Gong, Zhiguang Yan, Hao Rong, Hui Deng, Chen Zha, Cheng Guo, Lihua Sun, Chengzhi Peng, Xiaobo Zhu, and Jian-Wei Pan, “Realisation of high-fidelity nonadiabatic CZ gates with superconducting qubits,” npj Quant. Inf. 5
2019
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Rami Barends, CM Quintana, AG Petukhov, Yu Chen, Dvir Kafri, Kostyantyn Kechedzhi, Roberto Collins, Ofer Naaman, Sergio Boixo, F Arute, et al. , “Diabatic gates for frequency-tunable superconducting qubits,” Phys. Rev. Lett. 123
2019
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Riccardo Porotti, Dario Tamascelli, Marcello Restelli, and Enrico Prati, “Coherent transport of quantum states by deep reinforcement learning,” Commun. Phys. 2
2019
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Xiao-Ming Zhang, Zezhu Wei, Raza Asad, Xu-Chen Yang, and Xin Wang, “When does reinforcement learning stand out in quantum control? A comparative study on state preparation,” npj Quant. Inf. 5
2019
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Morten Kjaergaard, Mollie E Schwartz, Jochen Braumüller, Philip Krantz, Joel I-J Wang, Simon Gustavsson, and William D Oliver, “Superconducting qubits: Current state of play,” Annu. Rev. Condens. Matter Phys. 11
2020
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He-Liang Huang, Dachao Wu, Daojin Fan, and Xiaobo Zhu, “Superconducting quantum computing: a review,” Science China Information Sciences 63
2020
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Iris Paparelle, Lorenzo Moro, and Enrico Prati, “Digitally stimulated Raman passage by deep reinforcement learning,” Phys. Lett. A 384
2020
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Yu Guo and Lin Zhang, “Multipartite entanglement measure and complete monogamy relation,” Phys. Rev. A 101
2020
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Daniel Manzano, “A short introduction to the lindblad master equation,” AIP Advances 10
2020
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Alexandre Blais, Arne L. Grimsmo, S. M. Girvin, and Andreas Wallraff, “Circuit quantum electrodynamics,” Rev. Mod. Phys. 93
2021
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Pierpaolo Sgroi, G. Massimo Palma, and Mauro Paternostro, “Reinforcement learning approach to non-equilibrium quantum thermodynamics,” Phys. Rev. Lett. 126
2021
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Jonathon Brown, Pierpaolo Sgroi, Luigi Giannelli, Gheorghe Sorin Paraoanu, Elisabetta Paladino, Giuseppe Falci, Mauro Paternostro, and Alessandro Ferraro, “Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems,” New J. Phys. 23
2021
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2021
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Zheng An, Hai-Jing Song, Qi-Kai He, and DL Zhou, “Quantum optimal control of multilevel dissipative quantum systems with reinforcement learning,” Phys. Rev. A 103
2021
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Noticeable exceptions are discussed in Refs. Sgroi et al. 2021 ; Brown et al. 2021
2021
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Sanjib Ghosh, Tanjung Krisnanda, Tomasz Paterek, and Timothy CH Liew, “Realising and compressing quantum circuits with quantum reservoir computing,” Commun. Phys. 4
2021
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Luuk Coopmans, Di Luo, Graham Kells, Bryan K Clark, and Juan Carrasquilla, “Protocol discovery for the quantum control of majoranas by differentiable programming and natural evolution strategies,” PRX Quantum 2
2021
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Luigi Giannelli, Pierpaolo Sgroi, Jonathon Brown, Gheorghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Paladino, and Giuseppe Falci, “A tutorial on optimal control and reinforcement learning methods for quantum technologies,” Phys. Lett. A , 128054 (2022)
2022
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Pratibha Raghupati Hegde, Gianluca Passarelli, Annarita Scocco, and Procolo Lucignano, “Genetic optimization of quantum annealing,” Physical Review A 105
2022
Closest in time.