  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.
