Mapping Quantum Network Losses with Capacity Limits

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Author: Denis Avetisyan

A novel technique leverages network capacity regions to pinpoint loss probabilities, even amidst noisy channel conditions.

The networkā€™s fidelity degrades predictably with increasing channel error, transitioning from an ideal, noise-free stateā€”indicated by the baselineā€”to scenarios where all channels experience loss or bit-flip errors, with the most significant performance decline occurring when $QC_3QC_{3}$ exhibits both 30% loss and 30% bit-flip rates, demonstrating a compounding effect of these disturbances.
The networkā€™s fidelity degrades predictably with increasing channel error, transitioning from an ideal, noise-free stateā€”indicated by the baselineā€”to scenarios where all channels experience loss or bit-flip errors, with the most significant performance decline occurring when $QC_3QC_{3}$ exhibits both 30% loss and 30% bit-flip rates, demonstrating a compounding effect of these disturbances.

This review demonstrates how analyzing quantum capacity regions enables accurate loss characterization in quantum networks, even in the presence of Pauli errors, and validates the approach through network simulation using NetSquid.

Despite advances in quantum networking, comprehensive end-to-end characterization remains a significant challenge. This work, ‘Loss Tomography for Quantum Networks’, introduces a novel approach leveraging quantum capacity regions to directly determine loss probabilities across network channels. We demonstrate that analysis of these regions allows for accurate loss tomography, even amidst the complicating presence of bit-flip errors. Could this method offer a pathway towards more robust and efficient quantum network diagnostics and optimization?

The Inevitable Decay of Entanglement

Real-world quantum networks grapple with inherent noise and loss, degrading entanglement ā€“ a fundamental resource for quantum communication ā€“ and limiting performance. Unlike classical systems where errors are readily corrected, quantum information is fragile and susceptible to decoherence, complicating error correction. Accurate characterization of internal network errors ā€“ photon loss, depolarization, and bit-flip errors ā€“ is crucial for robust protocols and mitigating limitations. Without this understanding, optimizing network parameters and guaranteeing secure transmission becomes difficult. A network’s architecture, like any enduring structure, requires a thorough accounting of its vulnerabilities to withstand the currents of time.

The performance of a communication network degrades with increasing error rates, as evidenced by the contrast between the ideal noiseless network (dashed black) and networks subject to loss (blue), bit-flip errors (red), or a combination of both (green).
The performance of a communication network degrades with increasing error rates, as evidenced by the contrast between the ideal noiseless network (dashed black) and networks subject to loss (blue), bit-flip errors (red), or a combination of both (green).

Mapping the Networkā€™s Imperfections

Quantum Network Tomography offers a robust framework for characterizing errors through end-to-end measurements, diverging from methods that focus on individual components. Loss Tomography, a key application, quantifies qubit loss as they propagate through network channels by analyzing the success probabilities of transmitting known quantum states. Recent work demonstrates that loss probabilities can be inferred from quantum capacity regions, even with bit-flip errors, offering an efficient method for network characterization.

Simulating the Passage of Time

Network simulations were conducted using NetSquid, a discrete-event quantum network simulator, modeling a Rooted Tree Network topology to facilitate controlled investigation of entanglement distribution and swapping. The simulator allows precise configuration of network parameters, including node connectivity, transmission rates, and error models. Entanglement Swapping extends quantum communication beyond direct transmission by generating and distributing Bell Pairs between adjacent nodes. Simulations investigated the impact of both Homogeneous and Heterogeneous Loss Models on entanglement distribution rates to assess network resilience, collecting performance metrics under both conditions.

Performance Under Pressure

A quantum networkā€™s capacity is intrinsically linked to request arrival rates and scheduling policies. Suboptimal scheduling can increase latency and reduce success probabilities. Bit-flip errors, modeled by the Pauli X operator, represent a significant source of decoherence. Accurate loss estimation must account for these errors to provide a realistic assessment of channel performance and facilitate error correction. Recent experiments successfully inferred loss in Channel 2 (0.0985 Ā± 0.0084) and Channel 3 (0.2925 Ā± 0.0076) even with bit-flip errors, demonstrating the robustness of the characterization method. Sometimes observing the process is more valuable than attempting to accelerate it.

The pursuit of characterizing quantum networks, as detailed in this work, reveals a fundamental truth about all systems: their inevitable decay and the importance of understanding the mechanisms driving that decline. Determining loss probabilitiesā€”a core element of this researchā€”isnā€™t merely a technical exercise; it’s an attempt to map the aging process of the network itself. This resonates with a sentiment expressed by Richard Feynman: ā€œThe first principle is that you must not fool yourself ā€“ and you are the easiest person to fool.ā€ Accurate loss tomography, particularly in the face of Pauli errors, demands ruthless honesty in assessing the network’s true state, acknowledging imperfections not as failures, but as inherent aspects of its temporal existence. Every measured loss is a moment of truth in the timeline, a data point illustrating the networkā€™s progression from ideal to realized.

What Lies Ahead?

The pursuit of characterizing quantum networks, as demonstrated by this work, is fundamentally an exercise in applied archaeology. Each measurement of loss isnā€™t simply a number; itā€™s a fossil, hinting at the imperfections accumulated within the systemā€™s history. Versioning protocols, then, become a form of memory, allowing the network to retain a record of its degradation. The ability to discern loss probabilities even amidst the noise of bit-flip errors is a notable step, but it also highlights a crucial limitation: the assumption that these errors are known. The arrow of time always points toward refactoring, and future work will undoubtedly grapple with the challenge of characterizing unknown error models ā€“ the truly chaotic elements within these delicate systems.

Current methods primarily focus on establishing a static map of loss. A more nuanced understanding will require tracing the evolution of loss over time, acknowledging that channels arenā€™t fixed entities but dynamic landscapes. Simulators like NetSquid offer a controlled environment, but the true test lies in bridging the gap between simulation and the realities of deployed networks ā€“ where environmental factors, component drift, and unforeseen interactions will invariably introduce new forms of decay.

Ultimately, the field isnā€™t converging on a perfect solution, but rather developing a refined set of diagnostic tools. Each iteration brings a more detailed understanding of how these systems age, and a greater ability to anticipateā€”if not preventā€”their inevitable decline. The question isnā€™t whether a quantum network will fail, but how it will fail, and how gracefully it can do so.

Original article: https://arxiv.org/pdf/2511.07400.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-12 03:50