LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams
Quantum 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. Binary decision diagrams (BDDs) offer a scalable approach to this task, but the resulting BDDs and circuits depend heavily on variable ordering. Existing ordering heuristics commonly minimize BDD size because it is closely tied to the circuit size. However, BDD size is an imperfect proxy for the quantum cost of the synthesized circuit (QCC). We propose \texttt{QuantumEvo}, an evolutionary framework that uses an LLM as a heuristic generator for
利用大模型生成启发式用于量子电路合成，优化可逆门电路的成本。
- 核心方法
- 提出了 exttt{QuantumEvo}框架，通过使用大语言模型（LLM）生成变量排序的启发式方法，优化BDD到量子电路的转换过程。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 如何有效地将布尔函数转换成量子设备上可执行的可逆量子电路，同时最小化量子成本。
- 关键实验
- 通过对比实验展示了与传统方法相比， exttt{QuantumEvo}可以显著减少量子电路合成的量子成本。
- 主要贡献
- 该框架能够生成更有效的变量排序启发式，从而合成成本更低的量子电路。
- 意义与局限
- 为量子电路设计提供了新的优化方法，可能加速量子算法的实际应用。局限在于LLM生成的启发式可能需要针对特定问题进一步调优。