Researchers from Harvard, Anthropic, and QuEra Computing developed an AI framework that uses Large Language Models (LLMs) to discover and optimize fault-tolerant quantum circuits. This framework significantly reduces the resource costs of core computational primitives, achieving up to 9,100x lower costs compared to hand-crafted baselines. The LLMs achieved this by proposing complete Python programs for circuits, which were then evaluated and refined through an evolutionary process, leading to the discovery of new co-design principles and circuit architectures.
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