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' SuggestionY. 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]S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum
Quantum State Tomography with Conditional Generative Adversarial Networks
preprint, (2020). [arXiv]N. Yoshioka, W. Mizukami, F. Nori
Neural-Network Quantum States for the Electronic Structure of Real Solids
preprint, (2020). [arXiv]S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum
Classification and reconstruction of optical quantum states with deep neural networks
preprint, (2020). [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].