"Methods and Applications of Quantum Control"

Who: Yue Ban, Department of Physical Chemistry, University of the Basque Country UPV/EHU, Spain

Place: Online seminar, Donostia International Physics Center

Date: Thursday, 11 February 2021, 12:00

Fast and high-precision qubit control is crucial for new quantum technologies, including quantum computing, quantum sensing, quantum machine learning. In this talk, I will introduce the recent progress of quantum control and quantum computing algorithms which we have achieved with the application of the techniques of Shortcuts to Adiabaticity (STA) and machine learning. We integrate STA in the design of the dynamical decoupling sequences that drives the interaction between the sensor and target signals, and achieve the error-resilient scheme [1]. Deep Reinforcement Learning (DRL) leads to robust digital quantum control with robustness against systematic errors [2]. The experiment [3] verifies the efficiency of the protocol and reveals a general framework of digital quantum control. Neural networks enable to characterize targets with minimal knowledge of the physical model, in regimes where the quantum sensor presents complex responses under the shot noise due to a reduced number of measurements [4]. We also propose a speed-up quantum perceptron [5] with STA where the control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. The above results show that the combination of STA and machine learning is helpful to design feasible speed-up protocols for quantum control.

[1] C. Munuera-Javaloy, Y. Ban, X. Chen, and J. Casanova, Robust Detection of High-Frequency Signals at the Nanoscale, Phys. Rev. Applied 14, 054054 (2020). [2] Y. Ding, Y. Ban, J. D. Martin-Guerrero, E. Solano, J. Casanova, and X. Chen, Breaking Adiabatic Quantum Control with Deep Learning, arXiv: 2009.04297.
[3] M. Ai, Y. Ding, Y. Ban, J. D. Martin-Guerrero, J. Casanova, J. Cui, Y. Huang, X. Chen, C.-F. Li, and G.-C Guo, Experimentally Realizing Efficient Quantum Control with Reinforcement Learning, arXiv: 2101.09020.
[4] Y. Ban, J. Echanobe, Y. Ding, R. Puebla, and J. Casanova, Neural-network- based parameter estimation for quantum detection, arXiv: 2012.07677.
[5] Y. Ban, X. Chen, E. Torrontegui, E. Solano, and J. Casanova, Speed-up Quantum Perceptron via Shortcuts to Adiabaticity, arXiv: 2003.09938.

Host: Roman Orus

ZOOM: https://dipc-org.zoom.us/j/85748995214

YouTube: https://youtu.be/MBthv36PMag

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