Guest Speaker: Dr. David Meyer, Professor of Mathematics at the University of California, San Diego

Quantum computing offers the possibility of efficient solutions to problems that may be classically intractable. Recently, these problems have included sampling from a probability distribution and inferring a relation from partial information (e.g., a recommender system), both of which fall into the class of data science or machine learning problems. An important characteristic of such problems and their solutions is the model for the data generating process, specifically whether it is classical or quantum. In the second part of the talk, we’ll analyze some data from a simple human behavior, answering survey questions, and discuss whether or not to model it quantum mechanically. I will (re)introduce all the quantum mechanics necessary for our analysis.

Guest Speaker: Dr. Alex Kunin, Creighton University Department of Mathematics

The information we gather about the world through our senses is encoded and transmitted through the spiking activity of neurons. Viewed this way, the activity of a large set of neurons can be considered a joint probability distribution over a large number of binary states (namely, the spiking or silence of each neuron). A basic challenge of neuroscience is to find a parsimonious description of this distribution, and information theory offers a way to do so, called the Maximum Entropy Principle. In this talk I will give a light introduction to information theory and (Shannon) entropy, explain the dominance of “low-order” correlations in the information content of the retina (aka why does the Ising model show up?), and offer an explanation by means of high school(ish) geometry. Along the way I will muse about the role of network structure in all of this.