Machine learning of high dimensional data on a noisy quantum processor

Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google's universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.

Automated summary

Quantum kernel methods have the potential to accelerate data analysis by efficiently learning relationships between input data points, but scaling to large circuits on noisy hardware remains a challenge. Experimentally implementing a quantum kernel classifier on real high-dimensional data from cosmology using Google's Sycamore processor shows comparable classification accuracy to noiseless simulation.