Title: Machine Learning Techniques Applied to Quantum Physics
Speaker: Prof. Franco Nori (RIKEN, Saitama, Japan; and the University of Michigan, Ann Arbor, USA)
Abstract:
This talk will provide an overview of some of our work on Machine Learning (ML) techniques applied to quantum Physics problems [1-10]. Special emphasis will be on [3,6,7,10]. Regarding [10]: Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances d≥2. Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning (RL), we identify the optimal bosonic set of code words (denoted here by RL code), which, surprisingly, is composed of the Fock states |2⟩ and |4⟩. As we show, the RL code, despite its approximate nature, successfully suppresses single-photon loss, reducing it to an effective dephasing process that well surpasses the break-even threshold. It may thus provide a valuable building block toward full error protection. 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] 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. Let. 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. Let. 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 Monte Carlo, PRX Quantum 3, 010324 (2022). [PDF] 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. Let. 131, 050601 (2023). [PDF][Link][arXiv]