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

1. 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]

2. A. Melkani, C. Gneiting, F. Nori
   Eigenstate extraction with neural-network tomography
   Phys. Rev. A 102, 022412 (2020). [PDF][Link][arXiv]
   Editors' Suggestion

3. 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]

4. 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]

5. 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.]

6. 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]

7. 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

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].