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Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P · 1998
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L. & Kolter, J. Z · 2001
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Quantum algorithm for linear systems of equations
Harrow, A. W., Hassidim, A. & Lloyd, S · 2009
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Learning multiple layers of features from tiny images (2009)
Krizhevsky, A., Hinton, G. et al · 2009
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Adversarial machine learning
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. & Tygar, J. D · 2011
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Evasion attacks against machine learning at test time
Biggio, B. et al · 2013
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Intriguing properties of neural networks
Szegedy, C. et al · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J. & Szegedy, C · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J. & Szegedy, C · 2014
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Deep learning
LeCun, Y., Bengio, Y. & Hinton, G · 2015
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From facial parts responses to face detection: A deep learning approach
Yang, S., Luo, P., Loy, C.-C. & Tang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I. & Bengio, S · 2016
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D. & Vladu, A · 2017
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Quantum machine learning
Biamonte, J. et al · 2017
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Quantum algorithm for systems of linear equations with exponentially improved dependence on precision
Childs, A. M., Kothari, R. & Somma, R. D · 2017
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Quantum autoencoders for efficient compression of quantum data
Romero, J., Olson, J. P. & Aspuru-Guzik, A · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K. & Vollgraf, R · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D. & Vladu, A · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. & Wagner, D · 2017
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Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M. & Van Der Maaten, L · 2017
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Detecting adversarial samples from artifacts
Feinman, R., Curtin, R. R., Shintre, S. & Gardner, A. B · 2017
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Making machine learning robust against adversarial inputs
Goodfellow, I., McDaniel, P. & Papernot, N · 2018
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Quantum generative adversarial networks
Dallaire-Demers, P.-L. & Killoran, N · 2018
Cited alongside, same era.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A. & Madry, A · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N. & Wagner, D · 2018
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. & Hein, M · 2020
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Robust data encodings for quantum classifiers
LaRose, R. & Coyle, B · 2020
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Highly accurate protein structure prediction with alphafold
Jumper, J. et al · 2021
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Quantum circuit optimization with deep reinforcement learning
Fösel, T., Niu, M. Y., Marquardt, F. & Li, L · 2021
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Recent advances in adversarial training for adversarial robustness
Bai, T., Luo, J., Zhao, J., Wen, B. & Wang, Q · 2021
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Buckman, J., Roy, A., Raffel, C. & Goodfellow, I · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. & Sabuncu, M · 2018
Cited alongside, same era.
Pennylane: Automatic differentiation of hybrid quantum-classical computations
Bergholm, V. et al · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J., Rosenfeld, E. & Kolter, Z · 2019
Cited alongside, same era.
Supervised learning with quantum-enhanced feature spaces
Havlíček, V. et al · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A. et al · 2019
Cited alongside, same era.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H. et al · 2019
Cited alongside, same era.
Defence against adversarial attacks using classical and quantum-enhanced boltzmann machines
Kehoe, A., Wittek, P., Xue, Y. & Pozas-Kerstjens, A · 2021
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Quantum support vector machines for continuum suppression in b meson decays
Heredge, J., Hill, C., Hollenberg, L. & Sevior, M · 2021
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Demonstration of quantum volume 64 on a superconducting quantum computing system
Jurcevic, P. et al · 2021
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Quantum noise protects quantum classifiers against adversaries
Du, Y., Hsieh, M.-H., Liu, T., Tao, D. & Liu, N · 2021
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Robustness verification of quantum classifiers
Guan, J., Fang, W. & Ying, M · 2021
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Optimal provable robustness of quantum classification via quantum hypothesis testing
Weber, M., Liu, N., Li, B., Zhang, C. & Zhao, Z · 2021
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Robust in practice: Adversarial attacks on quantum machine learning
Liao, H., Convy, I., Huggins, W. J. & Whaley, K. B · 2021
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A scalable and fast artificial neural network syndrome decoder for surface codes
Gicev, S., Hollenberg, L. C. & Usman, M · 2021
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Planting undetectable backdoors in machine learning models
Goldwasser, S., Kim, M. P., Vaikuntanathan, V. & Zamir, O · 2022
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A kernel-based quantum random forest for improved classification
Srikumar, M., Hill, C. D. & Hollenberg, L. C · 2022
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Challenges and opportunities in quantum machine learning
Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L. & Coles, P. J · 2022
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Experimental quantum adversarial learning with programmable superconducting qubits
Ren, W. et al · 2022
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Quantumising adversarial machine learning: Recent progress and future directions
West, M. et al · 2022
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Quantamising adversarial machine learning: progress, challenges and opportunities (2022)
T. West, M. et al · 2022
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IBM Quantum Road-map: https://research.ibm.com/blog/ibm-quantum-roadmap-2025
2025
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