Quantum networks represent the forefront of modern communication systems, offering promising advantages over classical communication. They leverage the unique properties of quantum mechanics, such as superposition and entanglement, to enable tasks impossible in classical computing. However, the current era of Noisy Intermediate-Scale Quantum (NISQ) devices presents significant challenges due to the susceptibility of quantum systems to noise, leading to errors and decoherence that degrade the quality of quantum communication. To measure quantum link fidelity, quantum network benchmarking technology repeatedly assesses the fidelity of each link and averages the measurement results. Existing measurement schemes suffer from a trade-off between measurement overhead and accuracy. To this end, we propose QuFM, which is an adaptive scheme that leverages Kalman filter modeling to determine whether the stable state of Quantum State Tomography is reached when performing fidelity measurement, thereby achieving early termination of iteration to reduce the number of measurements. Through extensive experiments, we demonstrate the effectiveness of QuFM in achieving accurate and efficient fidelity measurements, and its applicability to different error types and network topologies.
Quantum networks represent the forefront of modern communication systems, offering promising advantages over classical communication. They leverage the unique properties of quantum mechanics, such as superposition and entanglement, to enable tasks impossible in classical computing. However, the current era of Noisy Intermediate-Scale Quantum (NISQ) devices presents significant challenges due to the susceptibility of quantum systems to noise, leading to errors and decoherence that degrade the quality of quantum communication. To measure quantum link fidelity, quantum network benchmarking technology repeatedly assesses the fidelity of each link and averages the measurement results. Existing measurement schemes suffer from a trade-off between measurement overhead and accuracy. To this end, we propose QuFM, which is an adaptive scheme that leverages Kalman filter modeling to determine whether the stable state of Quantum State Tomography is reached when performing fidelity measurement, thereby achieving early termination of iteration to reduce the number of measurements. Through extensive experiments, we demonstrate the effectiveness of QuFM in achieving accurate and efficient fidelity measurements, and its applicability to different error types and network topologies.
Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.
As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. In this paper, we propose QLSel, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments demonstrate that QLSel significantly outperforms existing representative methods. Our codes and video are available at https://github.com/Secbrain/QLSel.
Quantum communication and information technologies are poised to transform secure data transmission, distributed quantum computing, and scalable networking by leveraging fundamental principles such as superposition, entanglement, and the no-cloning theorem. This survey presents a structured overview of the quantum information processing (QIP) landscape, spanning theoretical foundations to system-level architectures. We cover quantum states, unitary evolution, gates, and channels, leading to the mathematical framework of quantum operations and the effects of decoherence. Quantum error correction (QEC) is examined through block codes and fault-tolerant operations, along with its integration with entanglement purification to address challenges of noisy intermediate-scale quantum (NISQ) devices. Physical platforms, including gate-based, continuousvariable and measurement-based models, are analyzed with an emphasis on error mitigation strategies to preserve coherence. We also discuss advancements in quantum sensing, particularly phase estimation, and evaluate communication protocols such as quantum key distribution (QKD) and teleportation in terms of fidelity, efficiency, and resilience. At the system level, components such as quantum networks, repeaters, and routers are reviewed with attention to scalability and security. Each section concludes with lessons learned, reflecting on challenges, trade-offs, and open questions. The survey culminates in outlining future directions in hybrid QEC-sensing integration, AI-enhanced error correction, and topological architectures, offering a forward-looking perspective on the co-evolution of quantum technologies. By bridging theory, protocols, physical systems, and architectures, this work charts a comprehensive roadmap toward secure, scalable, and high-fidelity quantum communication.
Long-distance quantum networks are far more than mere channels for data transfer; they are sophisticated systems that harness the counterintuitive features of quantum mechanics to enable secure and high-performance communication over vast distances. In our study, we explore the underlying mechanisms of quantum connectivity by investigating phenomena such as superposition and entanglement, and their roles in sustaining robust information propagation. We focus on the interaction between these quantum effects and routing protocols strategies that, while inspired by classical networks, must overcome challenges associated with quantum decoherence and measurement uncertainty. To evaluate these interactions, we simulated distributed quantum networks with node counts ranging from 10 to 100 using novel routing strategies including Q-Learning, Bell State, and Entanglement Swapping protocols. Our analysis centers on key performance metrics that capture both quantum fidelity and operational efficiency. Notably, our results show that high quantum state fidelity is maintained across all network configurations, confirming the efficacy of entangled states in preserving the integrity of quantum information over long distances. Simultaneously, we observe that increased network complexity introduces trade-offs, such as higher computational overhead and amplified susceptibility to decoherence and error propagation. These findings underscore the necessity for advanced error-correction techniques and adaptive routing strategies to ensure scalability and robustness in long-distance quantum communication. Overall, our work provides critical insights into optimizing quantum superposition and entanglement in practical routing implementations, thereby paving the way for the future realization of a global quantum communication infrastructure.
The scalability of quantum communication networks is crucial for enabling secure information exchange and distributed quantum computing across global infrastructures. However, achieving scalability remains a significant challenge due to the effects of decoherence, photon loss, and fidelity degradation over extended distances. This paper performs a critical investigation into the scalability limits of quantum communication networks, focusing on the interplay between entanglement generation rates, end-to-end fidelity, and network parameters such as node separation, noise levels, and total distance. Leveraging the SeQUeNCe simulation framework, we model adaptable trapped-ion repeater networks to uncover fundamental insights into both homogeneous and heterogeneous configurations. Our findings reveal transformative insights that help reshape system designs for near term quantum networks. We identify maximum node separation thresholds critical for practical implementations, illuminate the non-linear degradation of fidelity with increasing distance, and expose how routing and swapping protocols influence entanglement success rates at scale. Most notably, we demonstrate that while additional repeaters can mitigate distance-induced failures, excessive node counts introduce compounding fidelity losses that threaten network viability under certain architectures. These results provide a roadmap for revolutionizing quantum networks, with actionable design strategies such as optimized node placement, advanced routing algorithms, and the adoption of cutting-edge qubit technologies like topological qubits. By addressing key bottlenecks in quantum communication scalability, our work lays the foundation for the next generation of global quantum networks.
We propose a quantum tomography scheme for pure states which adopts machine learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic state. We prove the validity of the scheme theoretically and perform numerically simulated experiments on several typical target quantum states such as W, cluster and dimer states. We find that the required number of measurements to meet the convergence criterion does not grow exponentially in the number of qubits, thus the scheme achieves high efficiency that is crucial for large-scale quantum states tomography in a laboratory.