Bio-Adaptive Quantum Error Correction: Immune-Inspired Priors Enable 22–65% Overhead Reduction in Surface-Code Decoding
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2025
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| _version_ | 1866901577526149120 |
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| author | Chuck, Crawley Robinson, Jorel Ndenga, Barack |
| author_facet | Chuck, Crawley Robinson, Jorel Ndenga, Barack |
| contents | <p>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.</p> <p>The repository includes:</p> <p>Python notebooks for Stim-based and PyMatching-based simulations,</p> <p>Clonal-expansion cache implementation,</p> <p>Scripts for reproducing figures and pseudothreshold plots,</p> <p>Documentation on integrating the biological prior into MWPM decoding.</p> <p>This work establishes a novel link between adaptive immunity and quantum error correction, offering a new paradigm for biologically inspired, efficient, and adaptive decoders.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17684948 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Bio-Adaptive Quantum Error Correction: Immune-Inspired Priors Enable 22–65% Overhead Reduction in Surface-Code Decoding Chuck, Crawley Robinson, Jorel Ndenga, Barack Quantum Error Correction, Surface Code, MWPM, Bio-Inspired Algorithms, Immune System, TCRβ CDR3, Bayesian Prior, Clonal Expansion, Adaptive Memory, Correlated Noise, Fault-Tolerant Quantum Computing, Open-Source Simulation <p>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.</p> <p>The repository includes:</p> <p>Python notebooks for Stim-based and PyMatching-based simulations,</p> <p>Clonal-expansion cache implementation,</p> <p>Scripts for reproducing figures and pseudothreshold plots,</p> <p>Documentation on integrating the biological prior into MWPM decoding.</p> <p>This work establishes a novel link between adaptive immunity and quantum error correction, offering a new paradigm for biologically inspired, efficient, and adaptive decoders.</p> |
| title | Bio-Adaptive Quantum Error Correction: Immune-Inspired Priors Enable 22–65% Overhead Reduction in Surface-Code Decoding |
| topic | Quantum Error Correction, Surface Code, MWPM, Bio-Inspired Algorithms, Immune System, TCRβ CDR3, Bayesian Prior, Clonal Expansion, Adaptive Memory, Correlated Noise, Fault-Tolerant Quantum Computing, Open-Source Simulation |
| url | https://doi.org/10.5281/zenodo.17684948 |