LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams
QuantumEvo uses an LLM to evolve BDD variable-ordering heuristics, achieving a 70.9% tie-or-win rate on quantum circuit cost versus baseline methods.
The QuantumEvo framework uses an LLM as a heuristic generator for quantum-cost-aware BDD variable ordering in reversible circuit synthesis, searching over heuristics initialized from multiple families and selecting them by downstream quantum circuit cost. The discovered heuristic HGA-QE modifies the sifting step inside a genetic algorithm and achieves a 70.9% tie-or-win rate against the per-function best baseline, with strict wins on 13.5% of functions. Advantages are clearer on benchmark suites not used for heuristic discovery.
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