用机器学习反推量子编译器优化策略，揭示隐藏的算法黑箱。
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
- 通过对比原始与编译电路结构差异，用神经网络识别优化手法。
- 在数千个电路上测试，单个优化项识别准确率最高达0.96。
- 适合关注量子编译安全、知识产权保护的研究者。
量子线路编译是将量子算法适配硬件约束的关键步骤，但常以“黑箱”形式运行，难以洞察专有系统或先进开源框架所采用的优化技术。由于量子比特技术存在根本差异，高效编译器设计成本高昂，也使其面临多种安全威胁。本文首次探索编译器保密性面临的挑战——反向工程编译方法。我们提出一种基于机器学习的简单框架，通过分析原始电路与编译后电路之间的结构差异，推断底层优化策略。动机有二：一是提升优化过程透明度，助力跨平台调试与性能调优；二是识别商业系统中可能受知识产权保护的优化手段。在数千个量子电路上的广泛评估表明，神经网络在检测优化流程方面表现最佳，个别优化项的F1分数高达0.96。本研究初步验证了该威胁的可行性，凸显了在此领域开展主动研究的必要性。
原文摘要 · Abstract (English)
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.
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