Automated near-term quantum algorithm discovery for molecular ground states
This work addresses the challenge of automating quantum algorithm design for near-term quantum computers, with potential applications in chemistry and other domains, though it is incremental as it builds on existing AI and quantum methods.
The researchers tackled the problem of designing quantum algorithms for molecular ground states by using an AI-driven platform called Hive to discover efficient heuristic algorithms, achieving significant reductions in quantum resources for molecules like LiH, H2O, and F2, and benchmarking them on a quantum computer to identify system requirements for chemical precision.
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises large language models to drive a highly distributed evolutionary process for discovering new algorithms. We focus on the ground state problem in quantum chemistry, and discover efficient quantum heuristic algorithms that solve it for molecules LiH, H2O, and F2 while exhibiting significant reductions in quantum resources relative to state-of-the-art near-term quantum algorithms. Further, we perform an interpretability study on the discovered algorithms and identify the key functions responsible for the efficiency gains. Finally, we benchmark the Hive-discovered circuits on the Quantinuum System Model H2 quantum computer and identify minimum system requirements for chemical precision. We envision that this novel approach to quantum algorithm discovery applies to other domains beyond chemistry, as well as to designing quantum algorithms for fault-tolerant quantum computers.