Quantum dynamics for algorithmic applications
|advisor:||Aurel Gábris, Ph.D.|
|branch of study:||MI_MM|
|description:||Quantum devices have are known to have the potential ability to solve problems more efficiently than classical ones . The initial discoveries have spurred the development of a new field at the intersection of physics, computer science and mathematics, ultimately aiming at developing practical hardware implementations of known and future applications. The main challenge on this endeavor are the presence of imperfections and losses generally attributed to open quantum systems.
The goal of the work is to develop new algorithms or extend existing ones to quantum systems that are inherently affected either by measurement or their environments, and to leverage these effects for an advantage. Possible scenarios to study include the generalization of the Boson Sampling algorithm  to Gaussian states , algorithms based on non-linear transformations induced by measurement and post-selection or feed-forward [4,5], and algorithms built from the asymptotic dynamics of open quantum systems . The exact direction will be chosen based on the specific expertise and interest of the candidate.
|references:|| M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. (Cambridge University Press, 2011).
 S. Aaronson and A. Arkhipov. The Computational Complexity of Linear Optics. Theor. Comput. Sci. 9, 143–252 (2013).
 C. S. Hamilton et al. Gaussian Boson Sampling. Phys. Rev. Lett. 119, 170501 (2017).
 E. Knill, R. Laflamme, and G. J. Milburn. A scheme for efficient quantum computation with linear optics. Nature 409, 46–52 (2001).
 A. Gilyén, T. Kiss, and I. Jex. Exponential Sensitivity and its Cost in Quantum Physics. Scientific Reports 6, 20076 (2016).
 J. Novotný, G. Alber, and I. Jex. Asymptotic properties of quantum Markov chains. J. Phys. A: Math. Theor. 45, 485301 (2012).
|last update:||29.05.2019 10:08:15|
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