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

Satwik Kundu, Swaroop Ghosh

TL;DR

This work investigates confidentiality risks in quantum circuit transpilation by showing that an external adversary can infer a compiler's optimization techniques from pairs of original and optimized circuits using a machine-learning framework. The authors generate thousands of random circuits, label them with the optimization passes applied, and train multi-label classifiers, finding the neural network achieves the strongest performance with per-pass F1-scores up to $0.96$. The study demonstrates that proprietary or advanced open-source optimization strategies leave identifiable structural fingerprints in transpiled circuits, exposing potential IP leakage. It highlights the need for secure compiler usage and further research into defenses and forensic tooling for cross-platform debugging.

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.

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

TL;DR

This work investigates confidentiality risks in quantum circuit transpilation by showing that an external adversary can infer a compiler's optimization techniques from pairs of original and optimized circuits using a machine-learning framework. The authors generate thousands of random circuits, label them with the optimization passes applied, and train multi-label classifiers, finding the neural network achieves the strongest performance with per-pass F1-scores up to . The study demonstrates that proprietary or advanced open-source optimization strategies leave identifiable structural fingerprints in transpiled circuits, exposing potential IP leakage. It highlights the need for secure compiler usage and further research into defenses and forensic tooling for cross-platform debugging.

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.
Paper Structure (17 sections, 2 figures, 2 tables, 1 algorithm)

This paper contains 17 sections, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Basics of Quantum Computing
  4. Quantum Circuit Compilation
  5. Related Works
  6. Adversarial Framework
  7. Threat Model
  8. Adversary Knowledge
  9. Proposed Methodology
  10. Optimization Pass-selection
  11. Circuit Generation
  12. Features, Dataset and Training
  13. Evaluation
  14. Experimental Setup
  15. Results and Analysis
  16. ...and 2 more sections

Figures (2)

  • Figure 1: High-level overview of the threat model demonstrating a scenario where a stealthy adversary can infer details about black-box circuit-optimization software from the original and optimized circuits.
  • Figure 2: Pipeline Overview: (1) Circuit pairs are generated based on a few preselected optimization passes; (2) High-level features are extracted from both circuits; (3) A dataset is built using these extracted features, where the input consists of features from the original and optimized circuits, and the label is a one-hot encoding of the optimization techniques (OT) applied; (4) A machine-learning model, such as a random forest, is trained on this dataset to perform multi-label classification (one-vs-all); (5) The model predicts the optimization passes ($p_i$) used by the compiler.