QCommE2E: An Open-Source Simulation of End-to-End Quantum Communication Systems

• URL: http://arxiv.org/abs/2605.24723v1
• Date: Sat, 23 May 2026 20:27:21 GMT
• Title: QCommE2E: An Open-Source Simulation of End-to-End Quantum Communication Systems
• Abstract summary: This paper presents QCommE2E as an open-source simulation framework for end-to-end quantum communication systems.<n>The primary objective is to develop a comprehensive framework that includes transmitters, receivers, communication channels, performance metrics, and visualization tools.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents QCommE2E as an open-source simulation framework for end-to-end quantum communication systems, with explicit tutorial emphasis. The primary objective is to develop a comprehensive framework that includes transmitters, receivers, communication channels, performance metrics, and visualization tools, to facilitate the systematic design, configuration, and analysis of experimental simulations for novel quantum communication architectures. As the primary use case, we walk through the current quantum channel comparison, which maps textbook quantum-information channels and reduced optical-fiber/free-space surrogates into a single executable benchmark. We describe the common density-matrix interface, the matched modulation and detection chain, and the exact role of the channel classes Depolarizing Channel, Dephasing Channel, Erasure Channel, Bosonic Channel, Turbulence Channel, and PMD Channel. We also explain the current visualization layer, which projects received states onto constellation and Bloch representations for qualitative inspection. To keep the implementation-faithful, we provide a summary of the baseline execution, which uses a square 16-QAM embedding, a pretty-good-measurement detector constructed from the same reference-state codebook, and BER/SER. Finally, we position the channel-comparison as an entry point for broader future work, including equalization, quantum autoencoder, learning-based, and system-level algorithm integration.

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