Quantum mechanics

Computational science

Algorithm

Quantum computer

Pure mathematics

Mathematical physics

Discrete mathematics

Theoretical computer science

Image processing

Hilbert space

Computer science

Scaling

Ansatz

Physics

Kernel (algebra)

Reproducing kernel Hilbert space

Scale space

Mathematics

Image (mathematics)

Computer engineering

Artificial intelligence

Geometry

Quantum

Qubit

npj Quantum Information

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.

Machine learning of high dimensional data on a noisy quantum processor