Experimental Quantum Embedding for Machine Learning

TL;DR

This paper demonstrates the experimental implementation of quantum embedding techniques for machine learning using quantum optics, ultra-cold atoms, and superconducting quantum computers, showing promising results for future hybrid quantum ML systems.

Contribution

It introduces experimental platforms for quantum data embedding, optimized with deep learning, and validates their effectiveness across multiple quantum technologies.

Findings

Quantum embedding works successfully in experimental setups.

Different quantum platforms can complement each other for embedding tasks.

The approach paves the way for hybrid quantum machine learning applications.

Abstract

The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, we implement these ideas by engineering two different experimental platforms, based on quantum optics and ultra-cold atoms respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. We perform also a similar analysis on the Rigetti superconducting quantum computer. Therefore, we find that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.