  We present a quantum kernel method for high-dimensional data analysis using
Google's universal quantum processor, Sycamore. This method is successfully
applied to the cosmological benchmark of supernova classification using real
spectral features with no dimensionality reduction and without vanishing kernel
elements. Instead of using a synthetic dataset of low dimension or
pre-processing the data with a classical machine learning algorithm to reduce
the data dimension, this experiment demonstrates that machine learning with
real, high dimensional data is possible using a quantum processor; but it
requires careful attention to shot statistics and mean kernel element size when
constructing a circuit ansatz. Our experiment utilizes 17 qubits to classify 67
dimensional data - significantly higher dimensionality than the largest prior
quantum kernel experiments - resulting in classification accuracy that is
competitive with noiseless simulation and comparable classical techniques.
