Quantum integrated sensing and communication (QISAC) is emerging as a key paradigm for next-generation quantum technologies, in which entanglement, coherence, and non-classical correlations are jointly exploited to enable secure communication, distributed sensing, and networked quantum functionalities. As quantum systems transition from laboratory demonstrations to deployable infrastructures, performance is increasingly limited not by individual components but by cross-layer interactions among hardware noise, channel loss, error-management strategies, and network architecture. This survey presents a system-level architectural framework for QISAC, focusing on how physical-layer constraints, quantum channel characteristics, and error-mitigation mechanisms jointly determine end-to-end performance and architectural feasibility in scalable quantum networks. We analyze representative quantum hardware platforms-including superconducting circuits, trapped ions, neutral atoms, photonic systems, and solid-state spin qubits-through the lens of coherence time, fidelity, interface capability, and technology readiness, emphasizing their implications for networked sensing and communication rather than device-level operation. The roles of quantum error correction (QEC) and entanglement purification are examined as complementary error-management layers, highlighting trade-offs among resource overhead, latency, and achievable entanglement fidelity under realistic noise and memory constraints. We further connect these architectural considerations to QISAC-enabled applications such as distributed quantum sensing, quantum key distribution (QKD), and repeater-assisted entanglement distribution, clarifying how hardware limitations and error-management choices propagate to application-level performance. By synthesizing noise, error management, and network co-design into a unified system perspective, this survey provides design-oriented insights and architectural guidelines for building scalable, noise-resilient QISAC networks.Quantum integrated sensing and communication (QISAC) is emerging as a key paradigm for next-generation quantum technologies, in which entanglement, coherence, and non-classical correlations are jointly exploited to enable secure communication, distributed sensing, and networked quantum functionalities. As quantum systems transition from laboratory demonstrations to deployable infrastructures, performance is increasingly limited not by individual components but by cross-layer interactions among hardware noise, channel loss, error-management strategies, and network architecture. This survey presents a system-level architectural framework for QISAC, focusing on how physical-layer constraints, quantum channel characteristics, and error-mitigation mechanisms jointly determine end-to-end performance and architectural feasibility in scalable quantum networks. We analyze representative quantum hardware platforms-including superconducting circuits, trapped ions, neutral atoms, photonic systems, and solid-state spin qubits-through the lens of coherence time, fidelity, interface capability, and technology readiness, emphasizing their implications for networked sensing and communication rather than device-level operation. The roles of quantum error correction (QEC) and entanglement purification are examined as complementary error-management layers, highlighting trade-offs among resource overhead, latency, and achievable entanglement fidelity under realistic noise and memory constraints. We further connect these architectural considerations to QISAC-enabled applications such as distributed quantum sensing, quantum key distribution (QKD), and repeater-assisted entanglement distribution, clarifying how hardware limitations and error-management choices propagate to application-level performance. By synthesizing noise, error management, and network co-design into a unified system perspective, this survey provides design-oriented insights and architectural guidelines for building scalable, noise-resilient QISAC networks.

IEEE Open Journal of the Communications SocietyJan 1, 20262 citations

SFTRAP: Satisfying Fidelity Threshold Routing and Adaptive Purification for Throughput Maximum in Quantum Network

Zhi Wang, Tao Gong, Yingpu Nian +6 more

The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.

IEEE Transactions on CommunicationsJan 1, 202612 citations

Enhancing Secrecy Capacity and Sensing Fidelity in IQSC via Entanglement Purification

Vikram Singh Thakur, Rai Shrijal Anjanir, Mayank Yadav +3 more

IEEE Globecom Workshops (GC Wkshps)Dec 8, 20250 citations

High-Dimensional Carrier-Assisted Entanglement Purification Based on Mutually Unbiased Bases

Zihua Song, Lin Chen, Yongge Wang

Distilling high-dimensional entanglement under general asymmetric Pauli noise remains challenging. For the unprocessed qutrit single-carrier recurrence studied here, severe noise asymmetry can prevent the convergence condition from being satisfied. In this paper, we investigate carrier-assisted entanglement purification protocols, namely CAEPP and mCAEPP, first for two-qutrit systems, and show that the unprocessed single-carrier recurrence is bottlenecked by marginal $X$ -error probabilities. To overcome this limitation, we introduce MUB-guided preprocessing that combines randomized inversion-pair symmetrization with deterministic maximum-line alignment. For any qutrit Pauli channel with initial fidelity $p_{00}>1/3$ , we prove that, conditioned on successful syndrome outcomes, the MUB-adapted recurrence converges for every sufficiently large fixed carrier number and that its fixed-point fidelity approaches one in the subsequent large-carrier limit. We further extend the algebraic carrier-assisted framework and the asymmetric-noise bottleneck to arbitrary qudit dimensions, and show that in prime-power dimensions the finite-field preprocessing gives the sufficient threshold $p_{00}>1/d$ . A certified equal-target comparison over a finite deterministic optimization domain further shows that neither the MUB-adapted strategy nor complete Pauli-label twirling uniformly dominates the other in finite-resource cost.Distilling high-dimensional entanglement under general asymmetric Pauli noise remains challenging. For the unprocessed qutrit single-carrier recurrence studied here, severe noise asymmetry can prevent the convergence condition from being satisfied. In this paper, we investigate carrier-assisted entanglement purification protocols, namely CAEPP and mCAEPP, first for two-qutrit systems, and show that the unprocessed single-carrier recurrence is bottlenecked by marginal $X$-error probabilities. To overcome this limitation, we introduce MUB-guided preprocessing that combines randomized inversion-pair symmetrization with deterministic maximum-line alignment. For any qutrit Pauli channel with initial fidelity $p_{00}>1/3$, we prove that, conditioned on successful syndrome outcomes, the MUB-adapted recurrence converges for every sufficiently large fixed carrier number and that its fixed-point fidelity approaches one in the subsequent large-carrier limit. We further extend the algebraic carrier-assisted framework and the asymmetric-noise bottleneck to arbitrary qudit dimensions, and show that in prime-power dimensions the finite-field preprocessing gives the sufficient threshold $p_{00}>1/d$. A certified equal-target comparison over a finite deterministic optimization domain further shows that neither the MUB-adapted strategy nor complete Pauli-label twirling uniformly dominates the other in finite-resource cost.

quant-pharXiv:2605.20958May 21, 20260 citations

Entanglement-Aware Adaptive Routing for Optimizing Distributed Quantum Computing over Quantum Interconnect Networks

Tianrong Ding

Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.

Oct 31, 20260 citations

X

-error probabilities. To overcome this limitation, we introduce MUB-guided preprocessing that combines randomized inversion-pair symmetrization with deterministic maximum-line alignment. For any qutrit Pauli channel with initial fidelity

p_{00}>1/3

, we prove that, conditioned on successful syndrome outcomes, the MUB-adapted recurrence converges for every sufficiently large fixed carrier number and that its fixed-point fidelity approaches one in the subsequent large-carrier limit. We further extend the algebraic carrier-assisted framework and the asymmetric-noise bottleneck to arbitrary qudit dimensions, and show that in prime-power dimensions the finite-field preprocessing gives the sufficient threshold

p_{00}>1/d

. A certified equal-target comparison over a finite deterministic optimization domain further shows that neither the MUB-adapted strategy nor complete Pauli-label twirling uniformly dominates the other in finite-resource cost.