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6 February 2023 Deep reinforcement learning for quantum multiparameter estimation
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Citing works (13 citations)
1. Luca Pezzè, "Machine learning for optical quantum metrology", Advanced Photonics 5(2), pg. , (2023); doi:10.1117/1.ap.5.2.020501
2. Seyed Shakib Vedaie, Archismita Dalal, Eduardo J. Páez et al., "Framework for learning and control in the classical and quantum domains", Annals of Physics 458, pg. 169471, (2023); doi:10.1016/j.aop.2023.169471
3. Leopoldo Sarra, Florian Marquardt, "Deep Bayesian experimental design for quantum many-body systems", Machine Learning: Science and Technology 4(4), pg. 45022, (2023); doi:10.1088/2632-2153/ad020d
4. Valeria Cimini, Mauro Valeri, Simone Piacentini et al., "Variational quantum algorithm for experimental photonic multiparameter estimation", npj Quantum Information 10(1), pg. , (2024); doi:10.1038/s41534-024-00821-0
5. Nadia Milazzo, Olivier Giraud, Giovanni Gramegna et al., "Principles of quantum functional testing", Physical Review A 108(2), pg. , (2023); doi:10.1103/physreva.108.022602
6. Francesco Scattarella, Domenico Diacono, Alfonso Monaco et al., "Deep learning approach for denoising low-SNR correlation plenoptic images.", Scientific reports 13(1), pg. 19645, (2023); doi:10.1038/s41598-023-46765-x
7. Jiahao Huang, Min Zhuang, Jungeng Zhou et al., "Quantum Metrology Assisted by Machine Learning", Advanced Quantum Technologies , pg. , (2024); doi:10.1002/qute.202300329
8. Roberto Memeo, Andrea Crespi, Roberto Osellame, "Micro-opto-mechanical glass interferometer for megahertz modulation of optical signals", Optica 11(2), pg. 178, (2024); doi:10.1364/optica.506669
9. M. Sanchez, C. Everly, P. A. Postigo, "Advances in machine learning optimization for classical and quantum photonics", Journal of the Optical Society of America B 41(2), pg. A177, (2024); doi:10.1364/josab.507268
10. Muhammad Junaid Arshad, Christiaan Bekker, Ben Haylock et al., "Real-time adaptive estimation of decoherence timescales for a single qubit", Physical Review Applied 21(2), pg. , (2024); doi:10.1103/physrevapplied.21.024026
11. Zhantao Chen, Cheng Peng, Alexander N Petsch et al., "Bayesian experimental design and parameter estimation for ultrafast spin dynamics", Machine Learning: Science and Technology 4(4), pg. 45056, (2023); doi:10.1088/2632-2153/ad113a
12. Carlos Cardoso-Isidoro, Francisco Delgado, "An Architecture Superposing Indefinite Causal Order and Path Superposition Improving Pauli Channels' Parameter Estimation", Symmetry 16(1), pg. 74, (2024); doi:10.3390/sym16010074
13. Bakr Ahmed Taha, Ali J. Addie, Adawiya J. Haider et al., "Exploring Trends and Opportunities in Quantum‐Enhanced Advanced Photonic Illumination Technologies", Advanced Quantum Technologies 7(3), pg. , (2024); doi:10.1002/qute.202300414
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Cited by 13 scholarly publications.
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KEYWORDS
Education and training

Quantum protocols

Quantum experiments

Quantum numbers

Quantum systems

Evolutionary algorithms

Quantum machine learning

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