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Monday, April 23, 2018 — 10:30 AM EDT

Barzan Mozafari, Department of Computer Science and Engineering
University of Michigan

Monday, April 23, 2018 — 1:00 PM EDT

Weicong Ma, Master’s candidate
David R. Cheriton School of Computer Science

Tuesday, April 24, 2018 — 1:00 PM EDT

Chunhao Wang, PhD candidate
David R. Cheriton School of Computer Science

We give a dissipative quantum search algorithm that is based on a novel dissipative query model. If there are $N$ items and $M$ of them are marked, this algorithm performs a fixed-point quantum search using $O(\sqrt{N/M}\log(1/\epsilon))$ queries with error bounded by $\epsilon$. In addition, we present a continuous-time version of this algorithm in terms of Lindblad evolution.

Tuesday, April 24, 2018 — 2:00 PM EDT

Chunhao Wang, PhD candidate
David R. Cheriton School of Computer Science

We present a quantum algorithm for simulating the dynamics of Hamiltonians that are not necessarily sparse. Our algorithm is based on the assumption that the entries of the Hamiltonian are stored in a data structure that allows for the efficient preparation of states that encode the rows of the Hamiltonian. We use a linear combination of quantum walks to achieve a poly-logarithmic dependence on the precision. 

Thursday, April 26, 2018 — 10:00 AM EDT

Amir-Hossein Karimi, Master’s candidate
David R. Cheriton School of Computer Science

Thursday, April 26, 2018 — 10:30 AM EDT

Rachel Pottinger, Department of Computer Science
University of British Columbia

Users are faced with an increasing onslaught of data, whether it's in their choices of movies to watch, assimilating data from multiple sources, or finding information relevant to their lives on open data registries. In this talk I discuss some of the recent and ongoing work about how to improve understanding and exploration of such data, particularly by users with little database background.

Friday, April 27, 2018 — 9:30 AM EDT

Lisa Elkin, Master’s candidate
David R. Cheriton School of Computer Science

Friday, April 27, 2018 — 11:30 AM EDT

Rafael Olaechea Velazco, PhD candidate
David R. Cheriton School of Computer Science

Software behavioural models, such as finite state machines, are used as an input to model checking tools to verify that software satisfies its requirements. As constructing such models by hand is time-consuming and error-prone, researchers have developed tools to automatically extract such models from systems’ execution traces. 

Monday, April 30, 2018 — 10:00 AM EDT

Bahareh Sarrafzadeh, PhD candidate
David R. Cheriton School of Computer Science

Monday, April 30, 2018 — 1:30 PM EDT

Lesley Istead, PhD candidate
David R. Cheriton School of Computer Science

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