[28] Michael Kearns. Efficient noise-tolerant learning from statistical queries. Journal of the ACM (JACM), 45(6):983-1006, 1998. 2, 6
[29] Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, and Dacheng Tao. Learnability of quantum neural networks. PRX Quantum, 2(4):040337, 2021. 2
[30] Aravind Gollakota and Daniel Liang. On the hardness of pac-learning stabilizer states with noise. Quantum, 6:640, 2022.
[31] Armando Angrisani and Elham Kashefi. Quantum local differential privacy and quantum statistical query model. arXiv:2203.03591, 2022. 2
[32] Youngseok Kim, Andrew Eddins, Sajant Anand, Ken Xuan Wei, Ewout Van Den Berg, Sami Rosenblatt, Hasan Nayfeh, Yantao Wu, Michael Zaletel, Kristan Temme, et al. Evidence for the utility of quantum computing before fault tolerance. Nature, 618(7965):500-505, 2023. 2
[33] Youngseok Kim, Christopher J Wood, Theodore J Yoder, Seth T Merkel, Jay M Gambetta, Kristan Temme, and Abhinav Kandala. Scalable error mitigation for noisy quantum circuits produces competitive expectation values. Nature Physics, pages 1-8, 2023. 2
[34] Nader H. Bshouty and Jeffrey C. Jackson. Learning DNF over the uniform distribution using a quantum example oracle. In Wolfgang Maass, editor, Proceedings of the Eighth Annual Conference on Computational Learning Theory, COLT, pages 118-127. ACM, 1995. 2, 3, 4
[35] Srinivasan Arunachalam and Ronald de Wolf. Optimal quantum sample complexity of learning algorithms. Journal of Machine Learning Research, 19(71):1-36, 2018. 2, 3, 4, 5
[36] Sébastien Bubeck, Sitan Chen, and Jerry Li. Entanglement is necessary for optimal quantum property testing. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS, pages 692-703. IEEE, 2020. 3
[37] Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, et al. Quantum advantage in learning from experiments. Science, 376(6598):1182-1186, 2022. 3, 7, 9
[38] Leslie G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984. 4
[39] Alex B Grilo, Iordanis Kerenidis, and Timo Zijlstra. Learning-with-errors problem is easy with quantum samples. Physical Review A, 99(3):032314, 2019. 4
[40] Pranab Sen. Random measurement bases, quantum state distinction and applications to the hidden subgroup problem. In 21st Annual IEEE Conference on Computational Complexity (CCC'06), pages 14-pp. IEEE, 2006. 5
[41] Kai-Min Chung and Han-Hsuan Lin. Sample efficient algorithms for learning quantum channels in PAC model and the approximate state discrimination problem. In Min-Hsiu Hsieh, editor, 16th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2021, July 5-8, 2021, Virtual Conference, volume 197 of LIPics, pages 3:1-3:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. 5
[42] Vitaly Feldman, Will Perkins, and Santosh Vempala. On the Complexity of Random Satisfiability Problems with Planted Solutions. arXiv e-prints, page arXiv:1311.4821, November 2013. 6, 7
[43] Vitaly Feldman. A General Characterization of the Statistical Query Complexity. arXiv e-prints, page arXiv:1608.02198, August 2016. 6
[44] Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh S Vempala, and Ying Xiao. Statistical algorithms and a lower bound for detecting planted cliques. Journal of the ACM (JACM), 64(2):1-37, 2017. 6, 7, 9
[45] Christopher Moore, Alexander Russell, and Leonard J Schulman. The symmetric group defies strong fourier sampling. SIAM Journal on Computing, 37(6):1842-1864, 2008. 7