# Why supervised learning with quantum circuits reduces to kernel methods

### APA

Schuld, M. (2021). Why supervised learning with quantum circuits reduces to kernel methods. Perimeter Institute. https://pirsa.org/21050018

### MLA

Schuld, Maria. Why supervised learning with quantum circuits reduces to kernel methods. Perimeter Institute, May. 19, 2021, https://pirsa.org/21050018

### BibTex

@misc{ pirsa_21050018, doi = {10.48660/21050018}, url = {https://pirsa.org/21050018}, author = {Schuld, Maria}, keywords = {Quantum Information}, language = {en}, title = {Why supervised learning with quantum circuits reduces to kernel methods}, publisher = {Perimeter Institute}, year = {2021}, month = {may}, note = {PIRSA:21050018 see, \url{https://pirsa.org}} }

Maria Schuld University of KwaZulu-Natal

## Abstract

With the race for quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit. While such "supervised quantum models" are sometimes called "quantum neural networks", their mathematical structure reveals that they are in fact kernel methods with kernels that measure the distance between data embedded into quantum states. This talk gives an informal overview of the link, and discusses the far-reaching consequences for quantum machine learning.