AI 中文总结
该研究针对FTDD框架优化内存管理与索引排序，提出Path索引启发式算法，实现含100量子比特的QFT电路稳定模拟，为量子电路经典模拟提供了高性能方案。
AI 中文摘要
张量网络（TNs）与决策图（DDs）结合，可利用结构冗余和拓扑纠缠在经典计算机上实现量子电路的高性能精确模拟。但提升Fast Tensor Decision Diagram（FTDD）这类混合工具的可扩展性，需解决张量收缩过程中严格的内存增长约束与复杂的节点管理问题。本文对FTDD框架提出硬件感知的架构优化，主要贡献有两点：一是通过固定内存模型、严格的节点生命周期跟踪，以及对指数型、静态型、混合型表大小策略的系统评估，彻底改造内部内存管理，以减少分配开销并控制内存增长；二是引入由收缩路径引导的新索引排序启发式算法Path，并对作为DD压缩重要因素的变量索引排序策略展开综合研究。通过在多种电路拓扑上对比原始字母数字排序、RCM与本文的Path启发式算法，结果表明索引置换对节点共享和图密度有显著影响。实验评估证实，优化后的FTDD引擎可将结构化量子工作负载下的内存消耗限制在可控范围，使QFT等最多含100个量子比特的电路能稳定执行。此外，本文还系统表征了多种量子基准测试中的执行时间、内存占用与拓扑权衡关系。
英文摘要
Combining Tensor Networks (TNs) and Decision Diagrams (DDs) provides a high-performance framework for the exact simulation of quantum circuits on classical computers by exploiting structural redundancies and topological entanglement. However, improving the scalability of hybrid tools such as the Fast Tensor Decision Diagram (FTDD) requires addressing strict memory growth constraints and complex node management during tensor contractions. In this paper, we propose a hardware-aware architectural optimization of the FTDD framework, with two main contributions: first, we overhaul the internal memory management through a fixed-footprint memory model, strict node lifecycle tracking, and a systematic evaluation of table-sizing policies (exponential, static, and hybrid) to reduce allocation overhead and control memory growth; second, we introduce Path, a new index-ordering heuristic guided by the contraction path, and conduct a comprehensive study of variable index-ordering strategies, an important factor for DD compression. By comparing the original alphanumeric ordering, RCM, and our Path heuristic across diverse circuit topologies, we show that index permutation strongly affects node sharing and diagram density. Experimental evaluation confirms that our optimized FTDD engine bounds memory consumption under structured quantum workloads, enabling stable execution of circuits such as QFT with up to 100 qubits. Furthermore, we systematically characterize execution time, memory footprint, and topological trade-offs across diverse quantum benchmarks.
