Fully Quantum Classifier

TL;DR

This paper introduces a quantum classifier that uses data re-uploading and quantum search to optimize parameters, achieving a quadratic speed-up over classical methods.

Contribution

It presents a novel quantum classifier with trainable parameters optimized via quantum search, demonstrating improved efficiency over classical approaches.

Findings

Achieves quadratic speed-up in parameter optimization

Uses quantum data re-uploading technique

Demonstrates potential for efficient quantum machine learning

Abstract

In this paper we present a supervised machine learning quantum classifier. It consists of a quantum data re-uploading classifier with binary trainable parameters, the optimal values of which are found by a quantum search algorithm. We show that we can reach the quadratic speed-up in optimization trainable parameters compared to classical brute force search.