COMPUTER SCIENCE

SEMINAR SERIES

Title: Sparsity and Weak Supervision in Quantum Machine Learning Friday, Nov/8/19 | 12pm-1pm | West Hall, W105 Speaker: Seyran Saeedi Affiliation: VCU Abstract: Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. Quantum

mechanics rules imply a drastic departure from the classical setting that usually comes with the idea of fundamentally improving the speed and/or security of certain algorithms. Although building universal large-scale quantum computers still seems a hard task to accomplish, much progress has been made from both sides of academia and industry. As building quantum computers is nearing the era of commercialization and reaching quantum supremacy, it is important to think of potential applications that we might benefit from. Among many applications of quantum computation, we focus on the emerging field of quantum machine learning. Machine Learning is the science and art of making computers learn from available data to solve problems where the exact sequence of steps in an algorithm is hard to develop by human. Quantum algorithms for machine learning problems started to emerge in recent years. Our focus is on predictive models for binary classification, and we focus on variants of SVM that we expect to be especially important when training data becomes so large that a quantum algorithm with a guaranteed speedup becomes useful. We present a quantum machine-learning algorithm for training Sparse Support Vector Machine, where we deal with large dataset with having sparse solution. We also present the first quantum semi-supervised algorithm, where we have still large dataset but only a small fraction is provided with labels. Bio: Seyran Saeedi is a Ph.D. student at Department of Computer Science, Virginia Commonwealth University.