Breaking Adiabatic Quantum Control with Deep Learning

• URL: http://arxiv.org/abs/2009.04297v1
• Date: Wed, 9 Sep 2020 13:45:30 GMT
• Title: Breaking Adiabatic Quantum Control with Deep Learning
• Authors: Yongcheng Ding, Yue Ban, Jos\'e D. Mart\'in-Guerrero, Enrique Solano, Jorge Casanova, Xi Chen
• Abstract summary: We find that DRL leads to robust digital quantum control with operation time bounded by quantum speed limits dictated by STA. Our results introduce a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.
• Score: 3.291834844920595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speed up by pulse control. Here, we select single-component control of qubits, resembling the ubiquitous two-level Landau-Zener problem for gate operation. We aim at obtaining fast and robust digital pulses by combining STA and DRL algorithm. In particular, we find that DRL leads to robust digital quantum control with operation time bounded by quantum speed limits dictated by STA. In addition, we demonstrate that robustness against systematic errors can be achieved by DRL without any input from STA. Our results introduce a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.

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