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OSU PROBABILITY SEMINARDepartment of Mathematics |
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OSU PROBABILITY SEMINARDepartment of Mathematics |
Day/Time/Room |
Speaker |
Title and abstract |
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Tuesday, October 6, 4:00pm GILK 115 |
Yevgeniy Kovchegov
Oregon State University |
"Intro to quantum probability and quantum computing I" Abstract. In this expository talk we will begin with quantum gates and circuits, qubits, density matrix and quantum probability, covering the Grover's search algorithm in the end. |
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Tuesday, October 13, 4:00pm GILK 115 |
Yevgeniy Kovchegov
Oregon State University |
"Intro to quantum probability and quantum computing II" Abstract. In this second talk we will concentrate on quantum Fourier transform and the phase estimation procedure. |
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Tuesday, October 20, 4:00pm GILK 115 |
Yevgeniy Kovchegov
Oregon State University |
"Intro to quantum probability and quantum computing III" Abstract. In this third talk we will cover quantum Fourier transform, the phase estimation procedure and quantum walks. |
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Tuesday, October 27, 4:00pm Postponed |
Postponed
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Tuesday, November 3, 4:00pm GILK 115 |
Chris Sinclair
University of Oregon |
"Random matrix theory" Abstract. I will present a (very) brief introduction of quantities of interest in random matrix theory and why probabilists should care. I will then go on to explain a couple of ensembles I have worked on, and how I came to study random matrix theory via number theory. |
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Tuesday, November 10, 4:00pm NO SEMINAR |
No seminar this week
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Tuesday, November 24, 4:00pm GILK 115 |
Robert Burton
Oregon State University |
"Uniformly Distributed Stationary Processes" Abstract. (joint with Aimee Johnson) A sequences is uniformly distributed if for any interval J contained in the unit circle the proportion of points in the interval J of the first N points in the sequence tends to the length of J as N goes to infinity. One way to get such a sequence is to sample an iid sequence of uniform [0,1) random variables. The Central Limit Theorem implies that, with probability one, such a sequence is uniformly distributed with rate 1/n^{1/2}. One can ask for better rates than this but no better than 1/N because the error in a single point of the first N in a very small interval about the point is arbitrarily close to 1/N. Classically, people have looked at similar constructions involving powers of 1/K that arise from an ergodic process called the adding machine or else the K-adic process. Another construction, is for quadratic numbers ( or at least easy algebraic numbers) and uses the continued fraction expansion. Both of these are not stationary but arise from stationary processes that we generalize and compute the rate of uniform distribution. These both come as a kind of dual to iid processes or else Markov processes designed to maximize entropy. Then, at the end, we come back to highly random Markov processes by re-thinking and modifying the problem. |
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Tuesday, December 1, 4:00pm GILK 115 |
Mark Kelbert
Swansea University, UK |
"Probabilistic representations for solutions of higher-order elliptic equations and polyharmonic functions" Abstract. We study the so-called Lauricella problem for higher-order elliptic operators of the type $L=(\Delta+V)^m$. A Feyman-Kac type representation of the solutions is presented and the bounds for the growth of the solutions are proved by probabilistic methods. |