{
  "id": "2407.13533",
  "version": "v1",
  "published": "2024-07-18T14:06:02.000Z",
  "updated": "2024-07-18T14:06:02.000Z",
  "title": "VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models",
  "authors": [
    "Yanling Lin",
    "Ji Guan",
    "Wang Fang",
    "Mingsheng Ying",
    "Zhaofeng Su"
  ],
  "categories": [
    "quant-ph"
  ],
  "abstract": "Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce \\textit{VeriQR}, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware's noisy impacts by incorporating random noise to formally validate a QML model's robustness. \\textit{VeriQR} supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, \\textit{VeriQR} can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess \\textit{VeriQR} using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by \\textit{VeriQR}, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-18T14:06:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum machine learning models",
      "robustness verification tool",
      "quantum hardwares noisy impacts",
      "qml models",
      "noisy intermediate-scale quantum era"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}