Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks

• URL: http://arxiv.org/abs/2012.06701v1
• Date: Sat, 12 Dec 2020 02:13:28 GMT
• Title: Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
• Authors: Jiahao Yao, Paul K\"ottering, Hans Gundlach, Lin Lin, Marin Bukov
• Abstract summary: Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices. We present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.
• Score: 2.5946789143276447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses a formidable challenge. Using reinforcement learning (RL), we present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. The hybrid policy is modeled by a deep autoregressive neural network to capture causality. We employ the algorithm to prepare the ground state of the nonintegrable quantum Ising model in a unitary process, parametrized by a generalized quantum approximate optimization ansatz: the RL agent solves the discrete combinatorial problem of constructing the optimal sequences of unitaries out of a predefined set and, at the same time, it optimizes the continuous durations for which these unitaries are applied. We demonstrate the noise-robust features of the agent by considering three sources of uncertainty: classical and quantum measurement noise, and errors in the control unitary durations. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.

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