Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 2025, 'A obustness fication Tool for uantum Machine Learning Models' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 403-421.
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AbstractAdversarial 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 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. 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, 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 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 VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.
Berta, M, Lami, L & Tomamichel, M 2025, 'Continuity of Entropies via Integral Representations', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1896-1908.
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Huang, Y, Gao, D, Ying, S & Li, S 2025, 'DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Li, S, Zhou, X & Feng, Y 2025, 'Benchmarking Quantum Circuit Transformation With QKNOB Circuits', IEEE Transactions on Quantum Engineering, vol. 6, pp. 1-15.
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Mohanty, N, Behera, BK, Ferrie, C & Dash, P 2025, 'A quantum approach to synthetic minority oversampling technique (SMOTE)', Quantum Machine Intelligence, vol. 7, no. 1.
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Abstract The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm’s usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of TelecomChurn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.
Wills, A, Lin, T-C & Hsieh, M-H 2025, 'Tradeoff Constructions for Quantum Locally Testable Codes', IEEE Transactions on Information Theory, vol. 71, no. 1, pp. 426-458.
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Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 1970, 'VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 403-421.
View/Download from: Publisher's site
View description>>
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 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. 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, 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 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 VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.
Beigi, S, Hirche, C & Tomamichel, M 2025, 'Some properties and applications of the new quantum $f$-divergences'.
Girardi, F, Oufkir, A, Regula, B, Tomamichel, M, Berta, M & Lami, L 2025, 'Quantum umlaut information'.
Girardi, F, Oufkir, A, Regula, B, Tomamichel, M, Berta, M & Lami, L 2025, 'Umlaut information'.
