Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits

Abstract

The rapid development of reliable Quantum Processing Units (QPU) opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the similarity between graph-structured data, based on the time-evolution of a quantum system. By encoding the topology of the input graph in the Hamiltonian of the system, the evolution produces measurement samples that retain key features of the data. We study analytically the procedure and illustrate its versatility in providing links to standard classical approaches. We then show numerically that this scheme performs well compared to standard graph kernels on typical benchmark datasets. Finally, we study the possibility of a concrete implementation on a realistic neutral-atom quantum processor.

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Cited by in corpus (6)

• Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
• Quantum Phase Recognition via Quantum Kernel Methods
• Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
• Ab-initio tree-tensor-network digital twin for quantum computer benchmarking in 2D
• Quantum Extremal Learning
• "Proper" Shift Rules for Derivatives of Perturbed-Parametric Quantum Evolutions