Title: Mutual Reinforcement between Quantum Technologies and Machine Learning
When: Tuesday, July 16, 2024, 12:00
Place: Department of Theoretical Condensed Matter Physics, Faculty of Sciences, Module 5, Seminar Room (5th Floor)
Speaker: Yue Ban, Universidad Carlos III de Madrid, Spain.
The meet of quantum physics and machine learning brings a lot of progress in both fields. In particular, the latter gets displayed in quantum science as: (i) the use of classical machine learning as a tool applied to quantum physics problems, (ii) the use of quantum resources such as superposition, entanglement to enhance the performance compared to their classical counterparts. In this talk, I will introduce our recent work on the mutual reinforcement between quantum technologies and machine learning. I will show that neural networks are valuable for quantum sensing and quantum metrology, leading to adaptive protocols for quantum detection with broad working regime and high accuracy [1-3]. Digital quantum simulation, as an extension of quantum control methodologies, establishes a robust framework for transferring edge state transfer in an SSH chain [4] and exploring ground states of Fermi-Hubbard model on honeycomb lattices [5]. Last but not least, I will present a new quantum machine learning algorithm on quantum kernel which is addressed by proposing a data re-uploading quantum neural network to identify the optimal embedding quantum kernel [6]. This strategy utilizes quantum neural network training to construct the corresponding kernel matrix, offering significantly improved efficiency.
References
- Y. Chen, Y. Ban, R. He, J.-M. Cui, Y.-F. Huang, C.-F. Li, G.-C. Guo, J. Casanova, A neural network assisted 171Yb+ quantum magnetometer. Npj Quantum Inf. 8, 152 (2022).
- Y. Ban, J. Echanobe, Y. Ding, R. Puebla, J. Casanova, Neural-network-based parameter estimation for quantum detection, Quantum Sci. Technol. 6, 045012 (2021).
- B. Varona-Uriarte, C. Munuera-Javaloy, E. Terradillos, Y. Ban, A. Alvarez-Gila, E. Garrote, and J. Casanova, Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model, Phys. Rev. Lett. 132, 150801 (2024).
- S. V. Romero, X. Chen, G. Platero, Y. Ban, Optimizing edge-state transfer in a Su-Schrieffer-Heeger chain via hybrid analog-digital strategies, Phys. Rev. Applied 21, 034033 (2024).
- J. Tang, R. Xu, Y. Ding, X. Xu, Y. Ban, M. Yung, A. Pérez-Obiol, G. Platero, X. Chen, Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity, arXiv:2405.09225.
- P. Rodriguez-Grasa, Y. Ban, M. Sanz, Training embedding quantum kernels with data re-uploading quantum neural networks, arXiv:2401.04642.
