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.
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.
The susceptibility to decoherence is significantly high in quantum computing systems, and the latter effect significantly compromises the computational reliability, particularly in Noisy Intermediate-Scale Quantum (NISQ) computers. Even though the existing mitigation strategies are currently focused on hardware-level error correction and pulselevel noise mitigation, system-level execution variables (by far, the most notable of them being waiting times caused by queues) can also contribute to the worsening of fidelity degradation, but this is hardly addressed. In its turn, this manuscript suggests an AI-based decoherence mitigation framework, in which the time spent in the queue is incorporated into fidelity prediction and scheduling. The framework elucidates the interrelations between quantum-state fidelity and waiting time through a physics-informed decoherence formulation, which is followed by the use of machine-learning to predict degradation of fidelity prior to circuits execution. Based on the estimated measures of fidelity, decoherence-aware scheduling algorithms are presented to either give priority or re-scheduling to quantum jobs that become highly disturbed by the coherence loss. The viability of integrating the AI-based predictive mechanisms into the system-level quantum computing processes is shown through simulation-based assessment, which indicates that the suggested methodology can improve the effective fidelity of the implemented quantum jobs as compared to the traditional scheduling approaches. The findings indicate that AI-supported software-level alleviation mechanisms can be used as an effective and efficient enhancement of the dependability of nearterm quantum computing systems due to their scalability and use of hardware as the basis of the error reduction approach.
Quantum teleportation channels can overcome the effects of photonic loss, a major challenge in the implementation of a quantum network over fiber. Teleportation channels are created by distributing an entangled state between two nodes, which is a probabilistic process requiring classical communication. This causes critical delays that can cause information loss as quantum data suffers from decoherence when stored in memory. In this work, we quantify the effect of decoherence on fidelity at a node in a quantum network due to the storage of qubits in noisy memory platforms. We model a memory platform as a buffer that stores incoming qubits waiting for the creation of a teleportation channel. Memory platforms are parameterized with decoherence rate and buffer size. We show that fidelity at a node is a linear sum of terms, exponentially decaying with time, where the decay rate depends on the decoherence rate of the memory platform. This allows us to utilize Laplace transforms to derive computable functions of average fidelity with respect to the load, buffer size, and decoherence rate of the memory platform. We prove that serving qubits in a Last In First Out order with pushout for buffer overflow management maximizes average fidelity. Last, we apply this framework to model a single repeater node to calculate the average fidelity of the end-to-end entanglement created by this repeater assuming perfect gate operations.
Quantum entanglement is the cornerstone of quantum network communication, yet the qubit resources required to establish it are severely limited. Moreover, users within the network impose strict delay constraints on entanglement requests due to various factors, such as the real-time requirements of upper-layer applications or the finite coherence time of userside quantum memories. Consequently, effectively scheduling scarce qubit resources to serve these time-sensitive requests presents a significant challenge. This difficulty is exacerbated by the partial observability of quantum systems and the time-varying nature of qubit generation rates, rendering traditional scheduling algorithms ineffective. Furthermore, while Deep Reinforcement Learning (DRL) offers a promising avenue for quantum network optimization, existing research primarily focuses on network-layer routing, overlooking the finer-grained entanglement scheduling problem subject to strict delay and resource constraints. To bridge this gap, we propose the Deep Quantum Request Scheduler (DQRS), a novel DRL-based algorithm designed to maximize service quality under these dual constraints. Specifically, DQRS integrates a spatio-temporal branching architecture and virtual action slots within an ActorCritic framework. This design enables robust scheduling with continuous actions while adaptively managing fluctuating qubit budgets. Extensive simulation results demonstrate that DQRS achieves up to 44 % performance improvement over traditional methods and a 4 % gain compared to state-of-the-art DRL benchmarks across various resource constraints and observability settings, validating the robustness of the proposed approach.
A fundamental challenge in generating and distributing high-fidelity entanglement in quantum repeater networks is the management and suppression of quantum errors. In this work, we develop an approach for extending the effective coherence time of shared entanglement between nodes by utilizing compact quantum error correcting (QEC) codes, which are scheduled and run locally on resource-constrained repeaters, to maximize the fidelity of the shared state, while also accounting for the incurred gate noise from QEC circuit operations. This local QEC approach stabilizes the distributed state via local operations, so independent execution on the nodes requires no additional synchronization or protocol overhead. Simulation results show significant improvements in the effective coherence time relative to idle decay, suggesting this approach could be useful in several quantum networking protocols such as entanglement generation, swapping, purification, and teleportation.
Quantum Entanglement and Quantum swapping are major research areas nowadays.Remote quantum entanglement is used in many applications like secure communication, secret sharing, data aggregation, and precision sensing.In data aggregation applications, every sensor node captures data and communicates to the central node.Efficient Data aggregation depends on whether the local information or global quantum network information is used for constructing the aggregation schedules.In addition, Quantum networks suffer from lossy optical links and with limited resources such as quantum memories, edge capacities.Computation of optimal schedules deals with large quantities of data and complex time-consuming calculations.However, the quantum memories cannot hold the qubits for a longer time as the stored qubit completely decoheres an infinite amount of time.Hence, there is a necessity for finding new data aggregation scheduling protocols, which use optimal channel capacity and optimal size of memory for improving the network throughput.This paper uses a reinforcement-learning technique that considers entanglement pairing and swapping the success probability of nodes with their neighbors while finding an optimal scheduling policy.The proposed method uses local network information for constructing optimal data aggregation schedules by prior sharing the maximally entangled qubit pairs between the nodes through optimal usage of the processes, channel capacity, and memory at the intermediate nodes.Experiments show that our proposed method can maximize the network throughput.
Emerging quantum networks enable distributed quantum computing (DQC) by interconnecting modest, noisy quantum processors. However, practical deployments face tight memory budgets, probabilistic entanglement generation, and decoherence, all while serving multiple users. In realistic settings -- cloud execution, iterative/interactive workloads and concurrent jobs -- circuits do not arrive in a single batch but as a stream. This creates a fundamentally online problem: the system must react to stochastic arrivals, balance latency and throughput, and remain stable under varying loads without foresight of future jobs.
We study Online Scheduling and Distribution of Quantum Circuits (OS-DQC) and formalize the online scheduling problem for quantum networks: circuits arrive over time and must be executed without knowledge of future arrivals. At each event time (arrival or completion), the system decides which waiting circuits to launch and how to place their qubits, while accounting for congestion from in-flight circuits and constraints due to finite memories and decoherence. The objective is to minimize the mean completion time and makespan under these physical and resource constraints.