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arXiv:2603.25482

Maximizing Qubit Throughput under Buffer Decoherence and Variability in Generation

Padma Priyanka, Avhishek Chatterjee, Sheetal Kalyani

quant-phcs.ITSubmitted Mar 26, 2026• Announced Mar 27, 2026• 0 citations
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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.

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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.
A queue-channel is a model that captures waiting time-dependent degradation of information bits—a scenario motivated by quantum communications and delay-sensitive streaming. Recent work has characterised the capacity of the erasure queue-channel [1], and other noise models encountered in quantum communications. In this paper, we study an erasure queue-channel with feedback, and ask after the optimal transmission strategy to minimize waiting-induced erasures. Specifically, we assume that instantaneous feedback of queue-length (or of the queue-channel output) is available at the transmitter, which can modulate the rate of Poisson transmissions into the queue-channel. We pose an optimal control problem using HJB-style equations to maximize the information capacity, when the transmitter can choose from a bounded set of transmission rates. We show (under a numerically verifiable condition) that the optimal transmission policy is a single-threshold policy of the bang-bang type. In other words, transmitting at the maximum (minimum) possible rate when the queue is below (above) a threshold, maximizes the information capacity of the erasure queue-channel with feedback.
We study the fundamental limits of classical communication using quantum states that decohere as they traverse through a network of queues. We consider a network of Markovian queues, known as a Jackson network, with a single source or multiple sources and a single destination. Qubits are communicated through this network with inevitable buffering at intermediate nodes. We model each node as a ‘queue-channel,’ wherein as the qubits wait in buffer, they continue to interact with the environment and suffer a waiting time-dependent noise. Focusing on erasures, we first obtain explicit classical capacity expressions for simple topologies such as tandem queue-channel and parallel queue-channel. Using these as building blocks, we characterize the classical capacity of a general quantum Jackson network with waiting time-dependent erasures. Throughout, we study two types of quantum networks, namely, (i) Repeater-assisted and (ii) Repeater-less. We also obtain optimal pumping rates and routing probabilities to maximize capacity in simple topologies. More broadly, our work quantifies the impact of delay-induced decoherence on the fundamental limits of classical communication over quantum networks.
We investigate congestion-aware control of quantum repeater nodes operating under stochastic traffic and finite memory coherence. Entanglement generation is modeled as a probabilistic process producing Werner states subject to depolarizing memory decoherence, while entanglement requests arrive according to Poisson and bursty ON--OFF processes. Using a queueing-theoretic framework, we couple physical-layer memory dynamics with congestion-dependent service behavior to analyze stability, delay, and fidelity trade-offs. Operating regimes are characterized in terms of the load parameter, showing that fixed cutoff policies impose a fundamental fidelity--latency trade-off together with strict stability limits. Queue-aware adaptive control strategies are then introduced that dynamically adjust memory cutoff times and the number of parallel entanglement-generation channels. Cutoff adaptation restores stability near critical load by trading fidelity for service capacity, whereas resource scaling increases capacity without degrading entanglement quality. Under bursty traffic, joint adaptation suppresses delay spikes while activating additional channels only during congestion periods. The framework is further extended to a two-user shared-resource scenario in which independent traffic flows compete for a common resource pool. Stability is determined by aggregate load, while adaptive resource redistribution stabilizes queues that diverge under fixed partitioning. These results provide a queue-aware congestion-control perspective for adaptive resource management in quantum networks.
Quantum key distribution (QKD) relies on the precise control and optimization of quantum states for enhanced secure communication. This study focuses on optimizing quantum state evolution through external control fields while minimizing decoherence and energy consumption. The key challenge addressed in this research is the trade-off between achieving high fidelity in quantum state transitions while reducing the environmental loss impact, which significantly affect QKD performance. The study employs a mathematical framework based on the quantum master equation, incorporating system Hamiltonians, control Hamiltonians, and loss functions to model quantum state evolution. Optimization is achieved using an optimal control functional that balances fidelity and energy consumption. Additionally, key generation rate maximization, quantum error correction strategies, and channel loss modeling are incorporated to enhance QKD efficiency. Numerical simulations evaluate the secure key generation rate, error mitigation, and fidelity of quantum states over time. Results demonstrate that at an initial quantum state value of 0.50, the system undergoes an oscillatory decay, stabilizing near zero at t=10, indicating the impact of decoherence. Secure key generation analysis reveals that with an error rate of 0.1, the key rate is maximized at 0.72, but declines sharply to nearly zero when the error rate exceeds 0.5. Fidelity measurements indicate an exponential decline, starting at 1.0 at T=0T = 0 and reducing to approximately 0.61 at T=50T = 50 and 0.37 at T=100T = 100, reflecting the challenges of maintaining quantum coherence. Error correction strategies successfully mitigate errors, achieving a quantum error correction rate of nearly 99% in optimal conditions. The findings support policies aimed at enhancing quantum communication reliability by emphasizing robust error correction methods, efficient energy management, and adaptive control strategies to counteract decoherence. Future work should explore real-time adaptive optimization frameworks to further enhance QKD performance under varying noise conditions.
Quantum networking is causing a revolution in technology. It allows secure communication and spreads quantum computing through interactions based on entanglement. However, keeping qubit coherence and making sure transmission is high-quality are big problems. This is because of decoherence, the times spent waiting for entanglement, and the delays in synchronizing up. This study suggests an improved quantum memory system to tackle these issues. It does this through making entanglement in parallel using reinforcement learning to schedule jobs and advanced ways to stop decoherence. These include quantum error correction (QEC) and dynamic decoupling (DD). The study creates a math formula to make fidelity as good as possible. It shows that this problem can be solved in polynomial time. Tests show that this system improves how well fidelity is kept and how well quantum states are sent overall. This proves that the suggested system is a good way to create quantum networks that can grow and be trusted.
We consider a setting where qubits are processed sequentially, and derive fundamental limits on the rate at which classical information can be transmitted using quantum states that decohere in time. Specifically, we model the sequential processing of qubits using a single server queue, and derive explicit expressions for the capacity of such a `queue-channel.' We also demonstrate a sweet-spot phenomenon with respect to the arrival rate to the queue, i.e., we show that there exists a value of the arrival rate of the qubits at which the rate of information transmission (in bits/sec) through the queue-channel is maximized. Next, we consider a setting where the average rate of processing qubits is fixed, and show that the capacity of the queue-channel is maximized when the processing time is deterministic. We also discuss design implications of these results on quantum information processing systems.