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Patrick Huembeli
Patrick Huembeli
Extropic.ai
Verified email at extropic.ai
Title
Cited by
Cited by
Year
Identifying quantum phase transitions with adversarial neural networks
P Huembeli, A Dauphin, P Wittek
Physical Review B 97 (13), 134109, 2018
1302018
Unsupervised phase discovery with deep anomaly detection
K Kottmann, P Huembeli, M Lewenstein, A Acín
Physical Review Letters 125 (17), 170603, 2020
782020
Characterizing the loss landscape of variational quantum circuits
P Huembeli, A Dauphin
Quantum Science and Technology 6 (2), 025011, 2021
742021
Modern applications of machine learning in quantum sciences
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint arXiv:2204.04198, 2022
482022
Automated discovery of characteristic features of phase transitions in many-body localization
P Huembeli, A Dauphin, P Wittek, C Gogolin
Physical review B 99 (10), 104106, 2019
462019
QuCumber: wavefunction reconstruction with neural networks
MJS Beach, I De Vlugt, A Golubeva, P Huembeli, B Kulchytskyy, X Luo, ...
SciPost Physics 7 (1), 009, 2019
392019
Phase detection with neural networks: interpreting the black box
A Dawid, P Huembeli, M Tomza, M Lewenstein, A Dauphin
New Journal of Physics 22 (11), 115001, 2020
322020
Avoiding local minima in variational quantum algorithms with neural networks
J Rivera-Dean, P Huembeli, A Acín, J Bowles
arXiv preprint arXiv:2104.02955, 2021
232021
The physics of energy-based models
P Huembeli, JM Arrazola, N Killoran, M Mohseni, P Wittek
Quantum Machine Intelligence 4 (1), 1, 2022
132022
Entanglement Forging with generative neural network models
P Huembeli, G Carleo, A Mezzacapo
arXiv preprint arXiv:2205.00933, 2022
132022
Quadratic unconstrained binary optimization via quantum-inspired annealing
J Bowles, A Dauphin, P Huembeli, J Martinez, A Acín
Physical Review Applied 18 (3), 034016, 2022
102022
Hessian-based toolbox for reliable and interpretable machine learning in physics
A Dawid, P Huembeli, M Tomza, M Lewenstein, A Dauphin
Machine Learning: Science and Technology 3 (1), 015002, 2021
92021
Modern applications of machine learning in quantum sciences. 2022. doi: 10.48550
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint ARXIV.2204.04198, 0
9
Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme
A Gratsea, P Huembeli
Quantum Machine Intelligence 4 (1), 2, 2022
82022
Towards a heralded eigenstate-preserving measurement of multi-qubit parity in circuit QED
P Huembeli, SE Nigg
Physical Review A 96 (1), 012313, 2017
72017
Modern applications of machine learning in quantum sciences, arXiv e-prints
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint arXiv:2204.04198, 2022
62022
Towards a scalable discrete quantum generative adversarial neural network
S Chaudhary, P Huembeli, I MacCormack, TL Patti, J Kossaifi, A Galda
Quantum Science and Technology 8 (3), 035002, 2023
52023
Modern applications of machine learning in quantum sciences (2022)
A Dawid, J Arnold, B Requena, A Gresch, M Płodzien, K Donatella, ...
arXiv preprint arXiv:2204.04198, 0
5
PatrickHuembeli/Adversarial-Domain-Adaptation-for-Identifying-Phase-Transitions: DANN Arxiv Version 01
P Huembeli, A Dauphin, P Wittek
22017
The effect of the processing and measurement operators on the expressive power of quantum models
A Gratsea, P Huembeli
Quantum Machine Intelligence 5 (2), 32, 2023
12023
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