Wednesday, November 21, 2018

Wednesday, November 21, 2018 — 12:15 PM EST

Hemant Saxena, PhD candidate
David R. Cheriton School of Computer Science

We address the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of possible dependencies. However, distributed algorithms must also optimize data communication costs, especially in current shared-nothing settings. 

Wednesday, November 21, 2018 — 1:30 PM EST

Abel Molina, PhD candidate
David R. Cheriton School of Computer Science

Yao (1993) proved that quantum Turing machines and uniformly generated quantum circuits are polynomially equivalent computational models: t >= n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t, and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time.

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