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
      Quantum State Tomography with Conditional Generative Adversarial Networks
      Phys. Rev. Lett. 127, 140502 (2021). [PDF][Link][arXiv]

    7. 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]

    8. 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]

    9. 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]

    10. 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.]

    11. 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.]

    12. 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]

    13. 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]

    14. 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






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]