Dynamic Inhomogeneous Quantum Resource Scheduling with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework with a Transformer model for dynamic quantum resource scheduling, significantly enhancing real-time control and optimization in inhomogeneous quantum systems.

Contribution

It presents a novel RL-based approach using self-attention mechanisms for quantum resource scheduling, addressing the NP-hard optimization challenge.

Findings

Achieves over 3x improvement compared to rule-based agents

Develops a Transformer-based framework for real-time quantum scheduling

Enhances joint design of physical and control systems in quantum tech

Abstract

A central challenge in quantum information science and technology is achieving real-time estimation and feedforward control of quantum systems. This challenge is compounded by the inherent inhomogeneity of quantum resources, such as qubit properties and controls, and their intrinsically probabilistic nature. This leads to stochastic challenges in error detection and probabilistic outcomes in processes such as heralded remote entanglement. Given these complexities, optimizing the construction of quantum resource states is an NP-hard problem. In this paper, we address the quantum resource scheduling issue by formulating the problem and simulating it within a digitized environment, allowing the exploration and development of agent-based optimization strategies. We employ reinforcement learning agents within this probabilistic setting and introduce a new framework utilizing a Transformer model that emphasizes self-attention mechanisms for pairs of qubits. This approach facilitates dynamic scheduling by providing real-time, next-step guidance. Our method significantly improves the performance of quantum systems, achieving more than a 3$\times$ improvement over rule-based agents, and establishes an innovative framework that improves the joint design of physical and control systems for quantum applications in communication, networking, and computing.