Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Innovating machine learning with near-term quantum computing" Maria Schuld - University of KwaZulu-Natal & Xanadu Abstract: Algorithms that run on quantum computers - so-called quantum circuits - underlie different laws of information processing than conventional computations. By optimizing the physical parameters of quantum circuits we can turn these algorithms into trainable models which learn to generalize from data. This talk highlights different aspects of such "variational quantum machine learning algorithms", including their role in the development of near-term quantum technologies, their interpretation as a cross-breed of neural networks and support vector machines, strategies of automatic differentiation, and how to integrate quantum circuits with machine learning frameworks such as PyTorch and Tensorflow using open-source software.

"I will take my chances against a computer-generated decision any day of the week; it doesn't have the biases that people have [in making decisions]." When I saw this quote, it reminded me why artificial intelligence (AI) can be such a mixed bag for users. After all, despite its potential for problem-solving, AI is only as valuable as the data it has to work with. Here's a look at the good of AI, the red flags and how you can make sure this valuable tool has a positive effect on your business and customers. As the quote suggests, AI produces results unbiased by personal opinions, thereby provides a consistent customer experience (CX).