Researchers from Harvard Quantum, Anthropic and QuEra show new circuits cut qubit counts for quantum calculations

Researchers from Harvard Quantum Initiative for Science and Engineering, Anthropic, and QuEra Computing have developed an evolutionary framework using large language models to design quantum circuits. In seven of eight tested tasks, these LLM-designed circuits significantly reduced both Toffoli gate and qubit counts compared to existing designs. The work also uncovered a new reversible architecture for prime-field modular inversion, enabling the use of the divsteps algorithm, previously impractical for reversible integer arithmetic, and improving the trade-off between Toffoli gates and qubit counts. These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation. The efficiency of quantum circuits designed by large language models surpasses current methods in most tested scenarios, with circuits for seven of eight tasks exhibiting reductions in both Toffoli gate count and qubit requirements. This improvement is measured through a metric combining these…