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We demonstrate how one can use machine learning techniques to bypass the technical difficulties of designing an experiment and translating its outcomes into concrete claims about fundamental features of quantum fields.
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2013
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2018
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D. Grimmer, E. Brown, A. Kempf, R. B. Mann, and E. Martín-Martínez, A classification of open Gaussian dynamics, J. Phys. A 51
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2018
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A. Ortega, E. McKay, A. M. Alhambra, and E. Martín-Martínez
2019
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K. A. Landsman, C. Figgatt, T. Schuster, N. M. Linke, B. Yoshida, N. Y. Yao, and C. Monroe, Verified quantum information scrambling, Nature 567
2019
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E. G. Brown, E. Martín-Martínez, N. C. Menicucci, and R. B. Mann, Detectors for probing relativistic quantum physics beyond perturbation theory, Phys. Rev. D 87
2013
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2013
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D. M. T. Benincasa, L. Borsten, M. Buck, and F. Dowker, Quantum information processing and relativistic quantum fields, Class. Quantum Gravity 31
2014
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A. Ahmadzadegan, E. Martín-Martínez, and R. B. Mann, Cavities in curved spacetimes: The response of particle detectors, Phys. Rev. D 89
2014
Cited alongside, same era.
A. R. H. Smith and R. B. Mann, Looking inside a black hole, Class. Quantum Gravity 31
2014
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G. Adesso, S. Ragy, and A. R. Lee, Continuous variable quantum information: Gaussian states and beyond, Open Syst. Inf. Dyn. 21
2014
Cited alongside, same era.
E. Martín-Martínez, Causality issues of particle detector models in QFT and quantum optics, Phys. Rev. D 92
2015
Cited alongside, same era.
K. Yamaguchi, N. Watamura, and M. Hotta, Quantum information capsule and information delocalization by entanglement in multiple-qubit systems, Phys. Lett. A 383
2019
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A. Ahmadzadegan, F. Lalegani, A. Kempf, and R. B. Mann, Probing geometric information using the Unruh effect in the vacuum, Phys. Rev. D 100
2019
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V. Cimini, I. Gianani, N. Spagnolo, F. Leccese, F. Sciarrino, and M. Barbieri, Calibration of quantum sensors by neural networks, Phys. Rev. Lett. 123
2019
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K. Zhou, G. Endrődi, L.-G. Pang, and H. Stöcker, Regressive and generative neural networks for scalar field theory, Phys. Rev. D 100
2019
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A. Decelle, V. Martin-Mayor, and B. Seoane, Learning a local symmetry with neural networks, Phys. Rev. E 100
2019
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Z. Xu and A. del Campo, Probing the full distribution of many-body observables by single-qubit interferometry, Phys. Rev. Lett. 122
2019
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E. Martín-Martínez, T. R. Perche, and B. de S. L. Torres, General relativistic quantum optics: Finite-size particle detector models in curved spacetimes, Phys. Rev. D 101
2020
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E. S. Tiunov, V. V. T. (Vyborova), A. E. Ulanov, A. I. Lvovsky, and A. K. Fedorov, Experimental quantum homodyne tomography via machine learning, Optica 7
2020
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A. Lidiak and Z. Gong, Unsupervised machine learning of quantum phase transitions using diffusion maps, Phys. Rev. Lett. 125
2020
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M. N. Chernodub, H. Erbin, I. V. Grishmanovskii, V. A. Goy, and A. V. Molochkov, Casimir effect with machine learning, Phys. Rev. Research 2
2020
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E. Martín-Martínez, T. R. Perche, and B. de S. L. Torres, Broken covariance of particle detector models in relativistic quantum information, Phys. Rev. D 103
2021
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J. de Ramón, M. Papageorgiou, and E. Martín-Martínez, Relativistic causality in particle detector models: Faster-than-light signaling and impossible measurements, Phys. Rev. D 103
2021
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R. Lopp and E. Martín-Martínez, Quantum delocalization, gauge, and quantum optics: Light-matter interaction in relativistic quantum information, Phys. Rev. A 103
2021
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J. Carrasquilla and G. Torlai, How to use neural networks to investigate quantum many-body physics, PRX Quantum 2
2021
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E. Genois, J. A. Gross, A. Di Paolo, N. J. Stevenson, G. Koolstra, A. Hashim, I. Siddiqi, and A. Blais, Quantum-tailored machine-learning characterization of a superconducting qubit, PRX Quantum 2
2021
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D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk, V. A. Goy, S. D. Liubimov, and A. V. Molochkov, Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning, Phys. Rev. D 103
2021
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L. Jiang, L. Wang, and K. Zhou, Deep learning stochastic processes with QCD phase transition, Phys. Rev. D 103
2021
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J. Boeyens, S. Seah, and S. Nimmrichter, Uninformed Bayesian quantum thermometry, Phys. Rev. A 104
2021
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J. Polo-Gómez, L. J. Garay, and E. Martín-Martínez, A detector-based measurement theory for quantum field theory, Phys. Rev. D 105
2022
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F. S. Luiz, A. d. O. Junior, F. F. Fanchini, and G. T. Landi, Machine classification for probe-based quantum thermometry, Phys. Rev. A 105
2022
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S. Shi, K. Zhou, J. Zhao, S. Mukherjee, and P. Zhuang, Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics, Phys. Rev. D 105
2022
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E. Martín-Martínez, Quantum Mechanics in Phase Space: An introduction (2022), arXiv:2208.08682
2022
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P. Sekatski and M. Perarnau-Llobet, Optimal nonequilibrium thermometry in markovian environments, Quantum 6
2022
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2023
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H. Maeso-García, J. Polo-Gómez, and E. Martín-Martínez, How measuring a quantum field affects entanglement harvesting, Phys. Rev. D 107
2023
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