[8] Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd. Quantum support vector machine for big data classification. Physical review letters, 113(13):130503, 2014.
[9] I. Kerenidis and A. Prakash. Quantum recommendation systems. In 8th Innovations in Theoretical Computer Science Conference, ITCS, pages 49:1-49:21, 2017.
[10] Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209-212, 2019. 9
[11] Sam McArdle, András Gilyén, and Mario Berta. A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits. arXiv:2209.12887, 2022.
[12] Casper Gyurik, Chris Cade, and Vedran Dunjko. Towards quantum advantage via topological data analysis. Quantum, 6:855, 2022.
[13] Joran van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf. Convex optimization using quantum oracles. Quantum, 4:220, 2020.
[14] E. Tang. A quantum-inspired classical algorithm for recommendation systems. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC, pages 217-228, 2019. 1
[15] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. Quantum machine learning. Nature, 549(7671):195–202, 2017. 1
[16] M Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J Coles. Challenges and opportunities in quantum machine learning. Nature Computational Science, 2(9):567-576, 2022.
[17] Srinivasan Arunachalam and Ronald de Wolf. Guest column: A survey of quantum learning theory. ACM Sigact News, 48(2):41-67, 2017. 2
[18] Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. An introduction to quantum machine learning. Contemporary Physics, 56(2):172-185, 2015. 1
[19] Ryan O'Donnell and John Wright. Efficient quantum tomography. In Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, pages 899-912, 2016. 2
[20] Jeongwan Haah, Aram W Harrow, Zhengfeng Ji, Xiaodi Wu, and Nengkun Yu. Sample-optimal tomography of quantum states. In Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, pages 913-925, 2016.
[21] Scott Aaronson. Shadow tomography of quantum states. In Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, STOC, pages 325-338. ACM, 2018. 9
[22] Hsin-Yuan Huang, Richard Kueng, and John Preskil. Predicting many properties of a quantum system from very few measurements. Nature Physics, 16:1050-1057, 2020.
[23] Ashley Montanaro. Learning stabilizer states by bell sampling. arXiv:1707.04012, 2017.
[24] Srinivasan Arunachalam, Sergey Bravyi, Arkopal Dutt, and Theodore J Yoder. Optimal algorithms for learning quantum phase states. arXiv:2208.07851, 2022. 6, 7
[25] Hsin-Yuan Huang, Yu Tong, Di Fang, and Yuan Su. Learning many-body hamiltonians with heisenberg-limited scaling. Physical Review Letters, 130(20):200403, 2023.
[26] Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, and Jerry Li. Exponential separations between learning with and without quantum memory. In 62nd IEEE Annual Symposium on Foundations of Computer Science, pages 574-585. IEEE, 2021. 2, 3, 7, 8, 9
[27] Srinivasan Arunachalam, Alex B Grilo, and Henry Yuen. Quantum statistical query learning. arXiv:2002.08240, 2020. 2, 3, 4, 5, 6, 8