Discovering state-of-the-art quantum circuits with large language models

Key points

• Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms.1
• While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation.1
• By handling reversibility differently, the architecture makes divsteps -- a classical algorithm previously impractical for reversible integer arithmetic -- viable and substantially improves the Toffoli-qubit trade-off for modular inversion.1
• These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.1

Sources (1)

• [1]Discovering state-of-the-art quantum circuits with large language models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:59 PM

  Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them.

  Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms.

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