Automated near-term quantum algorithm discovery for molecular ground states

TL;DR

This paper introduces an AI-driven platform that discovers efficient quantum algorithms for molecular ground states, reducing quantum resource requirements and demonstrating practical implementation on quantum hardware.

Contribution

It presents a novel AI-based method using large language models and evolutionary algorithms to discover quantum algorithms for chemistry problems, with experimental validation.

Findings

Discovered quantum algorithms for LiH, H2O, and F2 with fewer resources.

Benchmarking on Quantinuum System Model H2 shows practical viability.

Identified key functions responsible for efficiency gains.

Abstract

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.