This week we have a talk by Ieva Cepaite, who is visiting from Edinburgh. She will tell us about her research in quantum machine learning. See below for title and abstract. 

Best,

-joe

Title: The Born Legacy: A Continuous Variable Quantum Born Machine 

Abstract: Quantum Machine Learning (QML) is a field on the rise for both the discrete and continuous Quantum Computing models. Many of the methods are ‘variational’ and heuristic in nature making them good candidates for implementation on so-called NISQ devices (noisy intermediate-scale quantum) which will likely be widely available to experiment on in the next few years. In my talk, I will present work that I have done in developing a QML method to learn continuous probability distributions, using the Continuous Variable (CV) Quantum Computing model. The method uses samples obtained from a distribution to train a Born Machine, a generative model built up from a quantum circuit which can then produce new samples by being measured. It promises to be exceptionally suited to learning distributions that originate from expectation values of quantum observables and furthermore the Born Machine circuit can then be used in other computations, should the need arise. I will also present a brief overview of the current state and potential future of QML for both the CV and discrete quantum computing models with a focus on neural networks.