[17] D. J. MacKay. Introduction to gaussian processes. NATO ASI Series F Computer and Systems Sciences, 1998.
[18] Benjamin Letham, Brian Karrer, Guilherme Ottoni, et al. Constrained bayesian optimization with noisy experiments. ArXiv e-prints, 2017.
[19] Donald R. Jones, Matthias Schonlau, and William J. Welch. Efficient global optimization of expensive black-box functions. J. of Global Optimization, 13(4):455-492, 1998.
[20] Diana M. Negoescu, Peter I. Frazier, and Warren B. Powell. The knowledge-gradient algorithm for sequencing experiments in drug discovery. INFORMS Journal on Computing, 23(3):346-363, 2011.
[21] Peter I. Frazier and Jialei Wang. Bayesian Optimization for Materials Design, pages 45-75. Springer International Publishing, 2016.
[22] Eric Brochu, Vlad M. Cora, and Nando de Freitas. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. ArXiv e-prints, 2010.
[23] Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc., 2012.
[24] T.L Lai and Herbert Robbins. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math., 6(1):4-22, 1985.
[25] Niranjan Srinivas, Andreas Krause, Sham Kakade, et al. Gaussian process optimization in the bandit setting: No regret and experimental design. In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML'10, page 1015-1022, 2010.
[26] H. J. Kushner. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Basic Engineering, 86(1):97-106, 1964.
[27] Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas. The application of Bayesian methods for seeking the extremum. Towards Global Optimization, 2(117-129):2, 1978.
[28] Philipp Hennig and Christian J. Schuler. Entropy search for information-efficient global optimization. J. Mach. Learn. Res., 13:1809-1837, 2012.
[29] José Miguel Henrández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani. Predictive entropy search for efficient global optimization of black-box functions. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1, NIPS'14, page 918-926, 2014.
[30] Peter Frazier, Warren Powell, and Savas Dayanik. The knowledge-gradient policy for correlated normal beliefs. INFORMS Journal on Computing, 21(4):599-613, 2009.
[31] David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro. A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes, 2008.
[32] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, USA, 2006.
[33] Christopher M Bishop and Nasser M Nasrabadi. Pattern recognition and machine learning, volume 4. Springer, 2006.
[34] Peter Auer. Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res., 3:397-422, 2003.
[35] S. Debnath, N. M. Linke, C. Figgatt, K. A. Landsman, K. Wright, and C. Monroe. Demonstration of a small programmable quantum computer with atomic qubits. Nature, 536(7614):63-66, 2016.
[36] D. Kielpinski, C. Monroe, and D. J. Wineland. Architecture for a large-scale ion-trap quantum computer. Nature, 417(6890):709-711, 2002.
[37] Walter Rudin. Principles of Mathematical Analysis. McGraw-Hill, 1964.
[38] Manpreet Singh Jattana, Fengping Jin, Hans De Raedt, and Kristel Michielsen. Improved Variational Quantum Eigensolver Via Quasidynamical Evolution. Phys. Rev. Applied, 19(2):024047, 2023.
[39] Subir Sachdev. Quantum phase transitions. Wiley Online Library, 2007.