# 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

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

Phys. Rev. Research, in press (2021). [arXiv]S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum

*Quantum state tomography with conditional generative adversarial networks*

Phys. Rev. Lett., in press (2021). [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].