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

 

 

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].