Joel Reardon, Assistant Professor
Department of Computer Science, University of Calgary
Zhucheng Tu, Master’s candidate
David R. Cheriton School of Computer Science
Lei Zou, Institute of Computer Science and Technology
Peking University
Barzan Mozafari, Department of Computer Science and Engineering
University of Michigan
Weicong Ma, Master’s candidate
David R. Cheriton School of Computer Science
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.
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.
Amir-Hossein Karimi, Master’s candidate
David R. Cheriton School of Computer Science
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.
Lisa Elkin, Master’s candidate
David R. Cheriton School of Computer Science