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This research presents a novel method for constructing syndrome-extraction circuits for qLDPC codes by leveraging inherent edge symmetries in their Tanner graphs, rather than analyzing the full code structure. The approach achieves provably optimal or near-optimal CNOT depth for Lifted Product and Balanced Product codes while delivering depth-optimal results for Quantum Tanner codes at scales approaching 600 data qubits. These findings significantly advance the practical feasibility of scalable fault-tolerant quantum computing by reducing circuit complexity without sacrificing error-correction capabilities.
Disassembling qLDPC codes for depth-optimal parity-check circuits
arXiv:2608.19917v1 Announce Type: new Abstract: Quantum low-density parity-check (qLDPC) codes offer a promising route to scalable fault-tolerant quantum computing, but their practical implementation requires efficient circuits for syndrome extraction. Many qLDPC families are assembled from a small set of components through explicit constructions that imprint edge symmetries on their Tanner graphs. We show that these symmetries can be exploited to design syndrome-extraction circuits from the underlying components, rather than from the full quantum code. For Lifted Product and Balanced Product codes this approach yields an analytical construction with provably optimal or near-optimal CNOT depth. For Quantum Tanner codes it produces depth-optimal circuits on every instance we test, including codes up to nearly 600 data qubits.
Abstract:Quantum low-density parity-check (qLDPC) codes offer a promising route to scalable fault-tolerant quantum computing, but their practical implementation requires efficient circuits for syndrome extraction. Many qLDPC families are assembled from a small set of components through explicit constructions that imprint edge symmetries on their Tanner graphs. We show that these symmetries can be exploited to design syndrome-extraction circuits from the underlying components, rather than from the full quantum code. For Lifted Product and Balanced Product codes this approach yields an analytical construction with provably optimal or near-optimal CNOT depth. For Quantum Tanner codes it produces depth-optimal circuits on every instance we test, including codes up to nearly 600 data qubits.
6 pages, 3 figures. Supplementary Material and GitHub code will be updated soon
arXiv:2608.19917 [quant-ph]
(or arXiv:2608.19917v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.19917 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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