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**Video URL**
http://pirsa.org/19070005

## Abstract

A device called a ‘Gaussian Boson Sampler’ has initially been proposed as a near-term demonstration of classically intractable quantum computation. But these devices can also be used to decide whether two graphs are similar to each other. In this talk, I will show how to construct a feature map and graph similarity measure (or ‘graph kernel’) using samples from an optical Gaussian Boson Sampler, and how to combine this with a support vector machine to do machine learning on graph-structured datasets. I will present promising benchmarking results and try to motivate why such a continuous-variable quantum computer can actually extract interesting properties from graphs.

## Details

**Talk Number**19070005

**Speaker Profile**Maria Schuld

**Collection**Machine Learning for Quantum Design

- Quantum Matter

**Scientific Area**

- Conference

**Talk Type**

**Subject**Condensed Matter

**Source Repository**PIRSA