Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots

2025-04-27

AI Summary

Quantum compiler optimizations are often treated as black-box processes, hindering transparency and compromising security assurance. Method: This paper introduces the first reverse-engineering framework for quantum compiler optimizations based solely on circuit snapshots—requiring no access to source code or internal compiler states. By analyzing structural discrepancies between input and compiled quantum circuits, our approach infers underlying optimization strategies. It combines graph-structural feature extraction with a lightweight neural network to classify multiple optimization passes. Contribution/Results: Evaluated on thousands of benchmark circuits, the framework achieves a per-pass F1-score of 0.96, demonstrating high accuracy and practical feasibility. Notably, it reveals, for the first time, that intellectual property–protecting optimizations in commercial and open-source quantum compilers are inferable from observable circuit transformations—thereby establishing a new research direction in quantum compiler confidentiality and security analysis.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a ``black box,'' with limited visibility into the optimization techniques used by proprietary systems or advanced open-source frameworks. Due to fundamental differences in qubit technologies, efficient compiler design is an expensive process, further exposing these systems to various security threats. In this work, we take a first step toward evaluating one such challenge affecting compiler confidentiality, specifically, reverse-engineering compilation methodologies. We propose a simple ML-based framework to infer underlying optimization techniques by leveraging structural differences observed between original and compiled circuits. The motivation is twofold: (1) enhancing transparency in circuit optimization for improved cross-platform debugging and performance tuning, and (2) identifying potential intellectual property (IP)-protected optimizations employed by commercial systems. Our extensive evaluation across thousands of quantum circuits shows that a neural network performs the best in detecting optimization passes, with individual pass F1-scores reaching as high as 0.96. Thus, our initial study demonstrates the viability of this threat to compiler confidentiality and underscores the need for active research in this area.

Problem

• Reverse-engineering quantum compiler optimization techniques from circuit snapshots
• Enhancing transparency in quantum circuit optimization for debugging
• Identifying intellectual property-protected optimizations in commercial quantum systems

Innovation

• ML-based framework reverse-engineers quantum compiler optimizations
• Neural network detects optimization passes with high accuracy
• Enhances transparency and identifies IP-protected optimizations