Large language models are beginning to generate quantum algorithms that match and, in some cases, surpass human-engineered methods, according to a blog post about new research from Quantinuum and Hiverge. The preliminary findings suggest this could lead to a shift in how quantum software may be developed. The work directly addresses a long-standing obstacle in quantum computing. Even as hardware improves, the algorithms needed to run real chemistry and materials problems remain difficult and expensive to design. Access to high-fidelity quantum processors is limited, so every extra circuit evaluation or unnecessary gate adds to the cost. Variational quantum algorithms, widely used for near-term chemistry simulations, require careful choices about which quantum operators to include, how to optimize their parameters, and how to keep noise from overwhelming the calculation. All of these steps tend to depend on expert judgment and weeks of trial-and-error, according to the post.

Full report : New research from Quantinuum and Hiverge shows that large language model–driven systems can automatically generate quantum chemistry algorithms that match or outperform leading human-designed methods while using far fewer quantum resources.

For more see the OODA Company Profile on Quantinuum.