aiqbench analysis
This study demonstrates how large language models can effectively automate the discovery of high-performance quantum low-density parity-check codes by evolving construction programs and search strategies. The approach successfully outperforms traditional random-mutation and hill-climbing methods, offering a new pathway for designing general quantum error correction schemes with certified distances.
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
arXiv:2606.24808v2 Announce Type: replace Abstract: Quantum low-density parity-check codes promise low-overhead error correction, but high-performing constructions remain hard to find. Here we introduce structured concept evolution, in which a large language model (LLM) evolves construction programs and their internal search strategies by modifying the finite group, protograph shapes or entry-assignment rule. We search for codes with fewer than $600$ data qubits and check weight at most $12$, optimizing $kd^2/n$ or the circuit-level logical error rate. Our method improves on published lifted-product benchmarks in selected parameter regimes, with exactly certified distances for the leading results. Circuit-level optimization typically yields lower logical error rates than $kd^2/n$ optimization, often despite smaller code distances. With the same evolution framework and step budget, the best LLM outperforms all random-mutation and hill-climbing runs on both objectives. Our results point to LLM-driven program search as an approach to designing general quantum LDPC codes.
Abstract:Quantum low-density parity-check codes promise low-overhead error correction, but high-performing constructions remain hard to find. Here we introduce structured concept evolution, in which a large language model (LLM) evolves construction programs and their internal search strategies by modifying the finite group, protograph shapes or entry-assignment rule. We search for codes with fewer than $600$ data qubits and check weight at most $12$, optimizing $kd^2/n$ or the circuit-level logical error rate. Our method improves on published lifted-product benchmarks in selected parameter regimes, with exactly certified distances for the leading results. Circuit-level optimization typically yields lower logical error rates than $kd^2/n$ optimization, often despite smaller code distances. With the same evolution framework and step budget, the best LLM outperforms all random-mutation and hill-climbing runs on both objectives. Our results point to LLM-driven program search as an approach to designing general quantum LDPC codes.
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
arXiv:2606.24808 [quant-ph]
(or arXiv:2606.24808v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.24808 Focus to learn more arXiv-issued DOI via DataCite
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