Learning distributions with quantum-enhanced generative models.
Date:
[TL;DR] Talk on generative learning with hybrid models on IBM hardware.
iHUB Quantum is a government organization (hosted at IISER Pune) that aims to bridge the gap between academia and industry in the quantum ecosystem. I was invited to speak at its FriQuant Seminar series as part of the World Quantum day (14 April) event. Before my talk, Prof. Rapol deivered an inspiring talk on how the field of quantum sensing, communication, and materials as grown multifold over the years. However, it is the field of computing that has gathered the headlines. With that introduction in his charismatic style, Prof. Rapol (whose classes I have attended as an undergraduate) introduced me to the audience.
The talk mainly focussed on highlighting the fundamental ingredients that enter into the soup of Quantum AI or Quantum machine learning: Bits, Neurons, and Qubits. The main focus of the talk was on introducing the buidling blocks in each field and how their synergy can be harnessed to build hybrid models executable on current hardware. With that, we discussed about generative models and how quantum models can perform better than classical models at learning distributions. The talk is available online on youtube on iHub’s channel. I enjoyed interacting with the experts, the inquistive audience and, not to forget, someone from the audience won exciting merchandise for the best question.
Here are a few links to the talk: