Jiatai Huang
Abstract
In this paper, we consider the problem of optimal scheduling for quantum switches with dynamic demand and random entanglement successes. Different from prior results that often focus on (known) fixed entanglement success probabilities, we assume zero prior knowledge about the entanglement success probabilities and allow them to vary from time to time in an adversarial manner. We propose a learning-based algorithm QSSoftMW based on the framework developed in [1], which combines adversarial learning and Lyapunov queue analysis. We show that QSSoftMW is able to automatically adapt to the changing system statistics and ensure quantum switch stability.
- J. Huang, L. Golubchik, and L. Huang. Queue scheduling with adversarial bandit learning. arXiv preprint arXiv:2303.01745, 2023.Google Scholar
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- D. T. N. Panigrahy, T. Vasantam and L. Tassiulas. On the capacity region of a quantum switch with entanglement purification. arXiv preprint arXiv:2212.01463, 2022.Google Scholar
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