Towards AI-enabled Control for Enhancing Quantum Transduction

TL;DR

This paper proposes an AI-enabled control system using deep reinforcement learning to optimize quantum transduction, facilitating better integration of quantum and optical devices for quantum internet applications.

Contribution

It introduces a novel AI-based approach that leverages deep reinforcement learning to enhance quantum transduction efficiency in real-time.

Findings

AI-enabled transducer improves qubit lifetime

Deep reinforcement learning optimizes wavelength conversion

Simulated environment training enhances real-world performance

Abstract

With advent of quantum internet, it becomes crucial to find novel ways to connect distributed quantum testbeds and develop novel technologies and research that extend innovations in managing the qubit performance. Numerous emerging technologies are focused on quantum repeaters and specialized hardware to extend the quantum distance over special-purpose channels. However, there is little work that utilizes current network technology, invested in optic technologies, to merge with quantum technologies. In this paper we argue for an AI-enabled control that allows optimized and efficient conversion between qubit and photon energies, to enable optic and quantum devices to work together. Our approach integrates AI techniques, such as deep reinforcement learning algorithms, with physical quantum transducer to inform real-time conversion between the two wavelengths. Learning from simulated environment, the trained AI-enabled transducer will lead to optimal quantum transduction to maximize the qubit lifetime.