Quantum information is fragile. It doesn’t take much for qubits, the basic information unit of quantum computing, to produce enough inaccuracies to scuttle a computation. Quantum error correction codes help solve this problem, but there are so many potential code formulations that finding the few useful ones is typically a time consuming, computationally demanding task. AI could offer a better way. In a new paper on arXiv, IBM researchers showcase an evolutionary workflow guided by large language models (LLMs) that quickly explores thousands of code variations, pushes forward the most compelling candidates, and analyzes their properties. The work is one example of the growing two-way interplay between quantum computing and classical AI, where each is beginning to inform and accelerate the other.

Full study : Researchers at IBM created an LLM-guided evolutionary framework that quickly found 465 distinct quantum error correction code candidates.