2025/11/19 by Tei, Kayo, Mishina, Haruto, Yamamoto, Naoki +1
Computer Science · Materials Science · #FOS: Computer and information sciences #Formal Methods in Verification #Machine Learning in Materials Science #Programming Languages (cs.PL) #Quantum Computing Algorithms and Architecture
paper · doi:10.48550/arxiv.2511.15581
openalex publication_date 2025/11/19 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28
Systematic discovery of optimization paths in quantum circuit simplification remains a challenge. Today, ZX-calculus, a computing model for quantum circuit transformation, is attracting attention for its highly abstract graph-based approach. Whereas existing tools such as PyZX and Quantomatic offer domain-specific support for quantum circuit optimization, visualization and theorem-proving, we present a complementary approach using LMNtal, a general-purpose hierarchical graph rewriting language, to establish a diagrammatic transformation and verification platform with model checking. Our methodology shows three advantages: (1) manipulation of ZX-diagrams through native graph transformation rules, enabling direct implementation of basic rules; (2) quantified pattern matching via QLMNtal extensions, greatly simplifying rule specification; and (3) interactive visualization and validation of optimization paths through state space exploration. Through case studies, we demonstrate how our framework helps understand optimization paths and design new algorithms and strategies. This suggests that the declarative language LMNtal and its toolchain could serve as a new platform to investigate quantum circuit transformation from a different perspective.