Work

Predicting fermionic densities using a projected-quantum-kernel method

Year: 2025

Type: article

Abstract: We use support vector regression based on a projected-quantum-kernel method to predict the density structure of one-dimensional fermionic systems of interest in quantum chemistry and quantum matter. The kernel is built upon with the observables of a quantum reservoir implementable with interacting Rydberg atoms. Training and test data of the fermionic system are generated using a density-functional theory approach. We test the performance of the method for several Hamiltonian parameters, finding a general common behavior of the error as a function of measurement time. At sufficiently long measurement times, we find that the method outperforms the classical linear kernel method and becomes competitive with the radial basis function method.

Source: Physical Review A

DOI: 10.1103/tdj2-wltj

Authors: F. Perciavalle, F. Plastina, M. Pisarra, N. Lo Gullo

Institutions: Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Cosenza, University of Calabria

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Ministero della Salute, Ministero dell'Università e della Ricerca

Awards: G1233008783, G1648780019, G3195958759