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.
- LLM-generated heuristics outperform BDD-size-minimizing ordering heuristics
- HGA-QE wins strictly on 13.5% of benchmark functions
- BDD size shown to be an imperfect proxy for quantum circuit cost
- Results generalize better on held-out benchmark suites
- Tags
- algorithm-discoverybinary-decision-diagramsevolutionary-searchllmquantum-computingreversible-circuits
- Products
- QuantumEvoHGA-QE
From the original article
1 min read at arxiv.orgQuantum circuits are central to implementing quantum algorithms on quantum devices, where quantum gates must be reversible. Many quantum algorithms rely on Boolean functions, which must therefore be implemented reversibly within quantum circuits. Reversible circuit synthesis provides a way to translate such Boolean functions into reversible circuits.
By Yoonju Sim · arxiv.org
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