Discovering state-of-the-art quantum circuits with large language models
LLM-guided evolutionary search finds quantum circuits that cut Toffoli and qubit costs on seven of eight tasks.
The paper presents an evolutionary framework in which large language models propose quantum circuit designs and an independent evaluator verifies and scores them. Across numerical computation, chemistry, optimization, quantum simulation, and number theory, discovered circuits cut both Toffoli and qubit counts versus prior art on seven of eight tasks. On the eighth, the search found a new reversible architecture for prime-field modular inversion that makes the classical divsteps algorithm practical under reversibility constraints.
- LLMs propose circuits and an independent evaluator scores them.
- Seven of eight tasks reduce Toffoli and qubit counts.
- New reversible design makes divsteps practical for modular inversion.
- Tasks include chemistry, optimization, simulation, and number theory.
From the original article
1 min read at arxiv.orgSignificant 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.
By Andi Gu · arxiv.org
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