Work

Quantum feature maps for graph machine learning on a neutral atom quantum processor

Year: 2023

Type: article

Abstract: Using a Rydberg quantum processor with up to 32 qubits, the authors implement machine learning tasks on data structured into graphs and show that their platform can distinguish two different graph connectivities. To illustrate the potential of such a method, they show that it can classify the toxicity of a given molecule based on a real-world biochemistry dataset.

Source: Physical Review A

DOI: 10.1103/physreva.107.042615

Authors: Boris Albrecht, Constantin Dalyac, Lucas Leclerc, Luis Ortiz-Gutiérrez, Slimane Thabet, Mauro D'Arcangelo, Julia R. K. Cline, Vincent E. Elfving, Lucas Lassablière, Henrique Silvério, Bruno Ximenez, Louis-Paul Henry, Adrien Signoles, Loïc Henriet

Institutions: Pasqal (France), Centre National de la Recherche Scientifique, Sorbonne Université, LIP6, Université Paris-Saclay, Institut d’Optique Graduate School, Laboratoire Charles Fabry

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Qubits à Atomes Froids pour la Simulation et le Calcul Quantiques

OpenAlex ID: W4366389470

FWCI: 2.956

Cites: 50

Cited by: 38

Related to: 10