Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation

TL;DR

The concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm, is introduced, conceptually based on a classical machine learning model and adopted to work with quantum data.

Abstract

We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm. The algorithm is conceptually based on a classical machine learning model and adopted to work with quantum data. We will motivate the theory and run the model as an error mitigation method for various parameters.

Figures (1)

• Figure 1: Average predicted fidelity vs. dataset size under different experimental parameters.