Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
This work provides an incremental improvement in quantum control for quantum computing researchers by offering a noise-robust method for optimizing quantum system parameters.
This paper addresses the challenge of optimizing both continuous and discrete parameters in variational quantum eigensolvers. They developed a hybrid policy gradient algorithm with a deep autoregressive neural network that simultaneously optimizes these parameters in an uncertainty-resilient manner. The algorithm was applied to prepare the ground state of the nonintegrable quantum Ising model, demonstrating robustness against classical and quantum measurement noise, and errors in control unitary durations.
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.