Machine learning of high dimensional data on a noisy quantum processor

Quantum kernel methods (QKMs) hold promise for accelerating data analysis by efficiently learning relationships between data points encoded in a high-dimensional Hilbert space. This research experimentally implements a quantum kernel classifier on real, high-dimensional cosmological data using Google's Sycamore processor. A circuit ansatz preserving kernel magnitudes and error mitigation techniques were employed. The experiment, using 17 qubits to classify 67-dimensional data, achieved test set classification accuracy comparable to noiseless simulations, demonstrating the potential of NISQ processors for high-dimensional data classification.

Evan Peters, João Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, Gabriel N. Perdue