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Entanglement-assisted Quasi-cyclic Quantum Low-density Pa...
[Submitted on 13 Jan 2025 (v1), last revised 26 Jul 2026 (this v · 2025-01-13 · via cs.IT updates on arXiv.org

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Abstract:We construct several families of entanglement-assisted quasi-cyclic quantum LDPC (EA-QC-QLDPC) codes via structured tilings of permutation matrices. The entanglement-unassisted portion of the joint Tanner graph of the proposed EA-QC-QLDPC code derived from two distinct classical QC-LDPC codes is free of 4-cycles. Notably, one of the proposed families constructed from two distinct classical codes requires only a \textit{single} shared Bell pair between the quantum transmitter and receiver, highlighting its resource efficiency. We also analytically determine the exact code rates for some of the proposed constructions. Furthermore, two of the proposed families of EA-QC-QLDPC codes are derived from a single classical code whose Tanner graphs possess girth greater than six, further enhancing their error-correcting performance.
We also propose an encoding scheme with improved complexity by exploiting the proposed code structure. The performance of the proposed codes is assessed under both random and burst error models under the depolarizing and Markovian noise actions. Simulation results reveal nearly one order of improvement in error-correction performance with the quaternary block-layered normalized min-sum (QBLNMS) decoder compared to the layered binary sum-product decoder over both depolarizing and Markovian channels. Using the QBLNMS decoder over a quaternary alphabet, we demonstrate that correlated Pauli errors can be effectively handled within the decoding framework.
Furthermore, under the QBLNMS decoding, the proposed codes achieve \textit{significant} performance improvements compared to prior works and can effectively handle both random and burst errors. The code constructions are scalable across various coding rates and quantum payloads, crucial for practical quantum communication and computing systems.

Submission history

From: Pavan Kumar [view email]
[v1] Mon, 13 Jan 2025 14:32:11 UTC (538 KB)
[v2] Mon, 3 Nov 2025 06:17:50 UTC (2,138 KB)
[v3] Sun, 26 Jul 2026 10:28:12 UTC (2,421 KB)