Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics

TL;DR

This paper introduces a novel optimal control framework for open quantum systems that leverages LSTM neural networks for dynamics prediction and employs the Adam algorithm for efficient control design, demonstrated on a two-level system.

Contribution

It presents a new control design method combining LSTM-based dynamics prediction with Adam optimization, enabling rapid and effective quantum control in noisy environments.

Findings

Fidelity improved in control optimization steps

LSTM accurately predicts open quantum system dynamics

Control scheme applicable to quantum computing and sensing

Abstract

The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically requires numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM-NNs) can accurately predict the time evolution of open quantum systems. Based on LSTM-NN predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As an exemplary example, we apply our scheme to design an optimal control for the adiabatic speedup in a two-level system under a non-Markovian environment. Our optimization procedure entails two steps: driving trajectory optimization and zero-area pulse optimization. Fidelity improvement for both steps have been obtained, showing the effectiveness of the scheme. Our optimal control design scheme utilizes predicted dynamics to generate optimized controls, offering broad application potential in quantum computing, communication, and sensing.