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Bulletin of the American Physical Society

Machine Learning Assisted Superconducting Qubit Readout – Part II: Deep Reinforcement Learning

Cole R. Hoffer, Benjamin Lienhard, Antti P. Vepsäläinen, Luke C. G. Govia, Vilhelm L. Andersen Woltz, David K. Kim, Alexander J. Melville, Bethany M. Niedzielski, Jonilyn Yoder, Thomas Ohki, Hari Krovi, Terry P. Orlando, William D. Oliver

Published Mar 18, 2021• 0 citations
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This paper investigates quantum gate control problems using the deep reinforcement learning algorithm, i.e., a model-free machine learning method. We implement the twin delayed deep deterministic policy gradient (TD3) algorithm to search for piece-wise constant control pulses for quantum gates through the trail interaction with the quantum system. Simulation results on four typical gates, including three one-qubit gates and a two-qubit CNOT gate, demonstrate that deep reinforcement learning exhibits improved performance for quantum gate control tasks. By setting punishment for steps in the reward function, DRL can automatically find a shorter control sequence than the traditional gradient-based algorithm (e.g., GRAPE algorithm) and the evolutionary algorithm (e.g., DE algorithm) while maintaining high control precision.
Applying deep reinforcement learning (DRL) to solve quantum control problems has become a popular research direction. However, the exploration capability and reward design for the learning agent, which usually affect the DRL’s application performance, has not been sufficiently emphasized. In this article, we propose a particle-assisted DRL (PDRL) method to address the above concern by enhancing exploration capabilities and designing appropriate reward functions for efficient quantum state manipulation. In PDRL, each episode in the quantum learning process is characterized by three kinds of events, i.e., unidentifiable, identifiable, and successful events. To improve exploration, exploration particles and feedback particles are employed in the early learning phase when episodes end in identifiable and successful events, respectively. To assign rewards, three event-based reward functions are provided for the DRL’s agent, exploration particles and feedback particles, respectively. Numerical results on single-qubit, two-qubit, and many-qubit systems validate the effectiveness of PDRL. Comparative results with existing DRL methods demonstrate the superior performance of PDRL for quantum state manipulation.