IBM is Using AI to Help Identify New Quantum Error Correction Codes

Searching for optimal Quantum Error Correction (QEC) codes is an incredibly time-consuming and computationally demanding bottleneck due to the vast space of potential algebraic formulations. To address this, IBM researchers have introduced OpenEvolve, an open-source, LLM-guided evolutionary AI framework that dramatically accelerates the discovery of viable QEC codes. The framework establishes a powerful, two-way interplay between classical AI and quantum computing. It utilizes large language models (LLMs) to generate informed hypotheses for algebraic expressions that could serve as valid code candidates.

Key Performance Results

The research team tested their framework by targeting bivariate bicycle (BB) codes—a class of quantum low-density parity check (qLDPC) codes featured on IBM’s fault-tolerant quantum computing roadmap.

QEC codes are formally evaluated using the format [[n, k, d]], where n represents physical qubits, k represents logical qubits, and d is the "distance" (error tolerance). In practice, maximizing these three parameters involves stark trade-offs. The evolutionary campaign successfully discovered 465 new error correction codes, showcasing diverse structural trade-offs. The table below shows a few examples of codes it found that provide different trade-offs, each of which might be advantageous for different situations.

Moving Forward

While further research is required to evaluate how these AI-generated codes perform in real-world physical architectures, OpenEvolve establishes a highly viable methodology for exploring massive algebraic code spaces. IBM Research has fully open-sourced the OpenEvolve library on GitHub, encouraging the global quantum research community to leverage and extend the framework for broader quantum error correction discovery.

Additional information on this research can be found in an IBM Research blog located here and also a preprint posted on arXiv here.

June 13, 2026