Pragmatic Quantum Machine Learning with Peter Wittek

Today we're joined by Peter Wittek, Assistant Professor at the University of Toronto working on quantum-enhanced machine learning and the application of high-performance learning algorithms in quantum physics. Peter and I caught up back in November to discuss a presentation he gave at re:Invent, "Pragmatic Quantum Machine Learning Today." In our conversation, we start with a bit of background including the current state of quantum computing, a look ahead to what the next 20 years of quantum computing might hold, and how current quantum computers are flawed. We then dive into our discussion on quantum machine learning, and Peter's new course on the topic, which debuted in February. I'll link to that in the show notes. Finally, we briefly discuss the work of Ewin Tang, a PhD student from the University of Washington, who's undergrad thesis "A quantum-inspired classical algorithm for recommendation systems," made quite a stir last summer. As a special treat for those interested, I'm also sharing my interview with Ewin as a bonus episode alongside this one. I'd love to hear your thoughts on how you think quantum computing will impact machine learning in the next 20 years! Send me a tweet or leave a comment on the show notes page.