Quantum feature maps for graph machine learning on a neutral atom quantum processor
Boris Albrecht, Constantin Dalyac, Lucas Leclerc, Luis Ortiz-Gutiérrez, Slimane Thabet, Mauro D'Arcangelo, Julia Cline, Vincent E. Elfving, Lucas Lassablière, Henrique Silvério, Bruno Ximenez, Louis-Paul Henry, Adrien Signoles, Loïc Henriet
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.
Topics & Concepts
GraphComputer scienceQuantum chemicalFeature (linguistics)QubitQuantumRydberg formulaQuantum computerQuantum machine learningTheoretical computer scienceArtificial intelligenceMoleculeQuantum mechanicsPhysicsIonIonizationLinguisticsPhilosophyQuantum Computing Algorithms and ArchitectureMachine Learning in Materials ScienceComputational Drug Discovery Methods