TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.7.1.013
TI  - A general learning scheme for classical and quantum Ising machines
PY  - 2024/03/14
UR  - https://www.scipost.org/SciPostPhysCore.7.1.013
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 7
IS  - 1
SP  - 013
A1  - Schmid, Ludwig
AU  - Zardini, Enrico
AU  - Pastorello, Davide
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCore.7.1.013,
	title={{A general learning scheme for classical and quantum Ising machines}},
	author={Ludwig Schmid and Enrico Zardini and Davide Pastorello},
	journal={SciPost Phys. Core},
	volume={7},
	pages={013},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.7.1.013},
	url={https://scipost.org/10.21468/SciPostPhysCore.7.1.013},
}