Dynamic Inhomogeneous Quantum Resource Scheduling with Reinforcement Learning
CoRR(2024)
摘要
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× 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.
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