Work

A general learning scheme for classical and quantum Ising machines

Year: 2024

Type: article

Abstract: An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent. We provide a mathematical characterization of the training process, which is based upon optimizing a loss function whose partial derivatives are not explicitly calculated but estimated by the Ising machine itself. Moreover, we present some experimental results on the training and execution of the proposed learning model. These results point out new possibilities offered by Ising machines for different learning tasks. In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning.

Source: SciPost Physics Core

DOI: 10.21468/scipostphyscore.7.1.013

Authors: Ludwig Schmid, Enrico Zardini, Davide Pastorello

Institutions: Technical University of Munich, University of Trento, Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications, University of Bologna

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond