[1] Aaronson, S. Shadow tomography of quantum states. SIAM Journal on Computing, 49(5), January 2020.
[2] Aaronson, S., Chen, X., Hazan, E., Kale, S., and Nayak, A. Online learning of quantum states. In NeurIPS '18: Proceedings of the 32nd International Conference of Neural Information Processing Systems, 2018.
[3] Agarwal, A., Dekel, O., and Xiao, L. Optimal algorithms for online convex optimization with multi-point bandit feedback. In COLT '10: Proceedings of the 23rd Annual Conference on Learning Theory, 2010.
[4] Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G., Buell, D. A., Burkett, B., Chen, Y., Chen, Z., Chiaro, B., Collins, R., Courtney, W., Dunsworth, A., Farhi, E., Foxen, B., Fowler, A., Gidney, C., Giustina, M., Graff, R., Guerin, K., Habegger, S., Harrigan, M. P., Hartmann, M. J., Ho, A., Hoffmann, M. R., Huang, T., Humble, T. S., Isakov, S. V., Jeffrey, E., Jiang, Z., Kafri, D., Kechedzhi, K., Kelly, J., Klimov, P. V., Knysh, S., Korotkov, A. N., Kostritsa, F., Landhuis, D., Lindmark, M., Lucero, E., Lyakh, D., Mandra, S., McClean, J. R., McEwen, M., Megrant, A., Mi, X., Michielsen, K., Mohseni, M., Mutus, J., Naaman, O., Neeley, M., Neill, C., Niu, M. Y., Ostby, E., Petukhov, A., Platt, J. C., Quintana, C., Rieffel, E. G., Roushan, P., Rubin, N. C., Sank, D., Satzinger, K. J., Smelyanskiy, V., Sung, K. J., Trevithick, M. D., Vainsencher, A., Villalonga, B., White, T., Yao, Z. J., Yeh, P., Zalcman, A., Neven, H., and Martinis, J. M. Quantum supremacy using a programmable superconducting processor. Nature, 2019.
[5] Audibert, J.-Y. and Bubeck, S. Regret bounds and minimax policies under partial monitoring. Journal of Machine Learning Research, 11:2635-2686, 2010.
[6] Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E. Gambling in a rigged casino: The adversarial multi-armed bandit problem. In Proceedings of the 36th Annual Symposium on Foundations of Computer Science, 1995.
[7] Beck, A. and Teboulle, M. Mirror descent and nonlinear projected subgradient methods for convex optimization. Operations Research Letters, 31(3):167-175, 2003.
[8] Bervoets, S., Bravo, M., and Faure, M. Learning with minimal information in continuous games. Theoretical Economics, 15:1471-1508, 2020.
[9] Bilenne, O., Mertikopoulos, P., and Belmega, E. V. Fast optimization with zeroth-order feedback in distributed multi-user MIMO systems. IEEE Trans. Signal Process., 68:6085-6100, October 2020.
[10] Brandao, F. G. S. L. and Svore, K. Quantum speed-ups for semidefinite programming, 2017.
[11] Bravo, M., Leslie, D. S., and Mertikopoulos, P. Bandit learning in concave  $N$ -person games. In NeurIPS '18: Proceedings of the 32nd International Conference of Neural Information Processing Systems, 2018.
[12] Bubeck, S. and Cesa-Bianchi, N. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning, 5(1):1-122, 2012.
[13] Bubeck, S., Lee, Y. T., and Eldan, R. Kernel-based methods for bandit convex optimization. In STOC '17: Proceedings of the 49th annual ACM SIGACT symposium on the Theory of Computing, 2017.
[14] Cesa-Bianchi, N. and Lugosi, G. Prediction, Learning, and Games. Cambridge University Press, 2006.
[15] Chakrabarti, S., Huang, Y., Li, T., Feizi, S., and Wu, X. Quantum Wasserstein generative adversarial networks. In NeurIPS '19: Proceedings of the 33rd International Conference on Neural Information Processing Systems, 2019.
[16] Chen, X., Hazan, E., Li, T., Lu, Z., Wang, X., and Yang, R. Adaptive online learning of quantum states, 2022.
[17] Chuang, I. and Nielsen, M. Quantum Computation and Quantum Information. Cambridge University Press, 2 edition, 2010.