# AI meets Quantum Physics: Machine Learning and Artificial Neural Networks applied to Quantum Physics

K. Bartkiewicz, C. Gneiting, A. Cernoch, K. Jirakova, K. Lemr, F. Nori

*Experimental kernel-based quantum machine learning in finite feature space*

Scientific Reports**10**, 12356 (2020). [PDF][Link_1][Link_2][arXiv]A. Melkani, C. Gneiting, F. Nori

*Eigenstate extraction with neural-network tomography*

Phys. Rev. A**102**, 022412 (2020). [PDF][Link][arXiv]*Editors' Suggestion*Y. Che, C. Gneiting, T. Liu, F. Nori

*Topological quantum phase transitions retrieved through unsupervised machine learning*

Phys. Rev. B**102**, 134213 (2020). [PDF][Link][arXiv]N. Yoshioka, W. Mizukami, F. Nori

*Solving quasiparticle band spectra of real solids using neural-network quantum states*

Communications Physics**4**, 106 (2021). [PDF][Link_1][Link_2][arXiv]Y. Nomura, N. Yoshioka, F. Nori

*Purifying Deep Boltzmann Machines for Thermal Quantum States*

Phys. Rev. Lett.**127**, 060601 (2021). [PDF][Link][arXiv][Suppl. Info.]S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum

*Quantum State Tomography with Conditional Generative Adversarial Networks*

Phys. Rev. Lett.**127**, 140502 (2021). [PDF][Link][arXiv]S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum

*Classification and reconstruction of optical quantum states with deep neural networks*

Phys. Rev. Research**3**, 033278 (2021). [PDF][Link][arXiv]E. Rinaldi, X. Han, M. Hassan, Y. Feng, F. Nori, M. McGuigan, M. Hanada

*Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo*

PRX Quantum**3**, 010324 (2022). [PDF][Link][arXiv]Y. Che, C. Gneiting, F. Nori

*Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths*

Phys. Rev. B**105**, 214205 (2022). [PDF][Link][arXiv]Y. Zeng, Z.Y. Zhou, E. Rinaldi, C. Gneiting, F. Nori

*Approximate Autonomous Quantum Error Correction with Reinforcement Learning*

Phys. Rev. Lett.**131**, 050601 (2023). [PDF][Link][arXiv][Suppl. Info.]E. Smolina, L. Smirnov, D. Leykam, F. Nori, D. Smirnova

*Identifying topology of leaky photonic lattices with machine learning*

Nanophotonics**13**, pp. 271-281 (2024). [PDF][Link][arXiv][Suppl. Info.]Z.C. Shi, J.T. Ding, Y.H. Chen, J. Song, Y. Xia, X.X. Yi, F. Nori

*Supervised learning for robust quantum control in composite-pulse systems*

Phys. Rev. Applied**21**, 044012 (2024). [PDF][Link][arXiv][MP4_1, 2]E. Rinaldi, M.G. Lastre, S.G. Herreros, S. Ahmed, M. Khanahmadi, F. Nori, C.S. Munoz

*Parameter estimation from quantum-jump data using neural networks*

Quantum Sci. Technol.**9**, 035018 (2024). [PDF][Link][arXiv]Y. Che, C. Gneiting, F. Nori

*Exponentially Improved Efficient and Accurate Machine Learning for Quantum Many-body States with Provable Guarantees*

preprint, (2024). [arXiv]

Related Presentations

Link: https://ntt-research.com/phi-franco-nori-2020summit-summary/

2020: Here is the video [MP4, Link] of Dr. Nori’s presentation on: “Using machine learning to solve challenging problems in quantum science and technology”, at the 2020 NTT Research Summit. The above presentation is very brief and intended for a non-technical audience. Far more information is available from our preprints on this topic, summarized in this [Link]. A poster of another work is available here [poster].

2021: Dr. Nori gave a plenary talk to students at the Summer School QIQT-21 (Quantum Information and Quantum Technology 2021): *"A few examples of Machine Learning and Artificial Neural Networks applied to Quantum Physics"*. This lecture lasted about 1 hour and 40 minutes. The video can be viewed on Youtube and MP4. [YoutTube, MP4]

Poster [PNG] (2MB) on the paper *"Topological quantum phase transitions retrieved through unsupervised machine learning"*, Phys. Rev. B **102**, 134213 (2020). [PDF][Link][arXiv]. Presented at the conference “Quantum Techniques in Machine Learning 2021”. [Link]