Bio-Adaptive Quantum Error Correction: Immune-Inspired Priors Enable 22–65% Overhead Reduction in Surface-Code Decoding
| dc.contributor.author | Charles L. Crawley II¹,Jorel Robinson², Barack Ndenga | |
| dc.date.accessioned | 2025-11-23T18:02:50Z | |
| dc.date.issued | 2025-11-22 | |
| dc.description | This dataset and code repository accompanies the publication introducing BA-QEC, the first quantum-error-correction decoder explicitly inspired by biological immune-system architecture. BA-QEC integrates a Bayesian prior derived from human TCRβ CDR3 length distributions and an adaptive clonal-expansion memory mechanism to improve decoding performance in topological quantum codes. Simulations of a distance-7 rotated surface code demonstrate 22% threshold improvement from the biological prior alone, and up to 61% enhancement when combined with clonal memory under temporally correlated (1/f-type) noise. All code is open-source (MIT license) and fully reproducible in <10 minutes on Google Colab. The repository includes: Python notebooks for Stim-based and PyMatching-based simulations, Clonal-expansion cache implementation, Scripts for reproducing figures and pseudothreshold plots, Documentation on integrating the biological prior into MWPM decoding. This work establishes a novel link between adaptive immunity and quantum error correction, offering a new paradigm for biologically inspired, efficient, and adaptive decoders. | |
| dc.description.abstract | Fault-tolerant quantum computation remains severely constrained by decoding overhead. Conventional minimum-weight perfect matching (MWPM) applied to the rotated surface code achieves a pseudothreshold of approximately 1.04% under circuit-level depolarizing noise, but only at the cost of substantial classical processing and qubit overhead. Here we introduce the first quantum-error-correction decoder that directly incorporates priors inspired by the human adaptive immune system. By using the empirically measured length distribution of T-cell-receptor (TCRβ) CDR3 regions as a Bayesian prior over error-chain plausibility, we modify PyMatching’s edge-weight model to obtain a 22% improvement in pseudothreshold on a distance-7 rotated surface code (100,000 shots per point). We further introduce a biologically motivated clonal-expansion mechanism: a cache of high-confidence syndrome–correction pairs that can be recalled in O(1) time when near-recurrent error patterns appear. Under temporally correlated (1/f-type) noise, this mechanism yields an additional 28–43% reduction in logical error rate, corresponding to total overhead reductions of 45–65% relative to MWPM. All code is open-source (MIT license) and fully reproducible in <10 minutes on free Google Colab. These results demonstrate that biological fault-tolerance architectures encode computational principles with direct applicability to quantum hardware, opening a new direction for bio-inspired quantum error correction. | |
| dc.description.provenance | Submitted by Barack Ndenga (ndengabarack@gmail.com) on 2025-11-23T18:02:50Z No. of bitstreams: 1 Bio-adaptive quantum decoder.pdf: 1634708 bytes, checksum: fe2f3ebda55358658a8479d2f915b643 (MD5) | en |
| dc.description.provenance | Made available in DSpace on 2025-11-23T18:02:50Z (GMT). No. of bitstreams: 1 Bio-adaptive quantum decoder.pdf: 1634708 bytes, checksum: fe2f3ebda55358658a8479d2f915b643 (MD5) Previous issue date: 2025-11-22 | en |
| dc.description.sponsorship | None | |
| dc.identifier.uri | https://africarxiv.ubuntunet.net/handle/1/10592 | |
| dc.language.iso | en | |
| dc.publisher | Publisher | |
| dc.title | Bio-Adaptive Quantum Error Correction: Immune-Inspired Priors Enable 22–65% Overhead Reduction in Surface-Code Decoding | |
| dc.type | Software |