University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Graph Analysis through Quantum Walks

Graph Analysis through Quantum Walks

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If you have a question about this talk, please contact Lars Kunze.

Classical random walks model a diffusion process on the vertex set of a graph, where transitions are allowed only along the graph edges, and have proven to be a useful tool in the analysis of its structure. More recently, there has been an increasing interest in using quantum walks to as an alternative to classical ones. Despite being similar in their definition, the dynamics of the classical and quantum walks can be remarkably different. For example, it has been shown that quantum parallelism and interference effects allow quantum walks to spread exponentially faster than their classical counterpart, over certain structures.

In this talk I will give a brief introduction to quantum walks and their use in graph analysis. In particular, I will show how to use the quantum Jensen-Shannon divergence between suitably defined quantum walks to perform a number of tasks, such as detecting the presence of structural symmetries in a graph, measuring the centrality of a vertex and defining a kernel on (un)attributed graphs. The experimental results demonstrate the effectiveness of these approaches and serve as a motivation to investigate further the rich expressive power of quantum walks in graph-based pattern recognition and network analysis.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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