Maximizing Qubit Throughput under Buffer Decoherence and Variability in Generation

2026-03-26

AI Summary

This study addresses the fundamental trade-off between throughput and fidelity in quantum communication networks, where qubits suffer from generation delays and buffer-induced decoherence. The authors formulate this challenge for the first time as an admission control problem in a finite-buffer queue, with rewards decaying as a function of waiting time. They theoretically characterize the optimality conditions for a “zero-delay” policy and propose an adaptive scheduling framework based on Bayesian online learning to handle unknown system parameters. The proposed approach significantly enhances throughput while preserving high fidelity, and its design principles are extensible to other delay-sensitive Internet-of-Things sensing and service systems.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Learning to Search
• Machine Learning: Quantum Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum communication networks require transmission of high-fidelity, uncoded qubits for applications such as entanglement distribution and quantum key distribution. However, current implementations are constrained by limited buffer capacity and qubit decoherence, which degrades qubit quality while waiting in the buffer. A key challenge arises from the stochastic nature of qubit generation, there exists a random delay (D) between the initiation of a generation request and the availability of the qubit. This induces a fundamental trade off early initiation increases buffer waiting time and hence decoherence, whereas delayed initiation leads to server idling and reduced throughput. We model this system as an admission control problem in a finite buffer queue, where the reward associated with each job is a decreasing function of its sojourn time. We derive analytical conditions under which a simple "no lag" policy where a new qubit is generated immediately upon the availability of buffer space is optimal. To address scenarios with unknown system parameters, we further develop a Bayesian learning framework that adaptively optimizes the admission policy. In addition to quantum communication systems, the proposed model is applicable to delay sensitive IoT sensing and service systems.

Problem

• qubit throughput
• buffer decoherence
• generation variability
• quantum communication networks
• stochastic generation delay

Innovation

• admission control
• qubit decoherence
• Bayesian learning
• throughput optimization
• quantum communication networks