Problem
Research questions and friction points this paper is trying to address.
quantum algorithm discovery
molecular ground states
near-term quantum computing
quantum chemistry
Automated near-term quantum algorithm discovery for molecular ground states
🤖 AI Summary
This work addresses the challenges of high resource consumption and algorithmic complexity in current quantum approaches to molecular ground-state estimation. We propose Hive, an AI-driven framework that integrates large language models with distributed evolutionary algorithms to enable, for the first time, the automated discovery of quantum heuristic algorithms. Applied to LiH, H₂O, and F₂ molecules, Hive generates highly efficient quantum circuits that substantially reduce quantum resource requirements. Through interpretability analysis, we uncover the mechanisms underlying performance gains. The discovered algorithms are experimentally validated on the Quantinuum H2 quantum processor, achieving chemical accuracy with fewer resources and precisely identifying the minimal hardware specifications necessary to reach this benchmark.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-driven algorithm discovery
quantum program synthesis
molecular ground state
resource-efficient quantum circuits
large language models
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