Quantum-Efficient Kernel Target Alignment

TL;DR

This paper proposes a quantum kernel learning method using the Nyström approximation to reduce quantum circuit executions, improving efficiency while maintaining classification accuracy in noisy quantum environments.

Contribution

It introduces a low-rank approximation approach for quantum kernels trained with Kernel Target Alignment, reducing quantum circuit requirements and enhancing robustness against noise.

Findings

Nyström method significantly reduces quantum circuit executions.

The approach maintains high classification accuracy across datasets.

Model shows robustness under various noise conditions.

Abstract

In recent years, quantum computers have emerged as promising candidates for implementing kernels. Quantum Embedding Kernels embed data points into quantum states and calculate their inner product in a high-dimensional Hilbert Space by computing the overlap between the resulting quantum states. Variational Quantum Circuits (VQCs) are typically used for this end, with Kernel Target Alignment (KTA) as cost function. The optimized kernels can then be deployed in Support Vector Machines (SVMs) for classification tasks. However, both classical and quantum SVMs scale poorly with increasing dataset sizes. This issue is exacerbated in quantum kernel methods, as each inner product requires a quantum circuit execution. In this paper, we investigate KTA-trained quantum embedding kernels and employ a low-rank matrix approximation, the Nystr\"om method, to reduce the quantum circuit executions needed to construct the Kernel Matrix. We empirically evaluate the performance of our approach across various datasets, focusing on the accuracy of the resulting SVM and the reduction in quantum circuit executions. Additionally, we examine and compare the robustness of our model under different noise types, particularly coherent and depolarizing noise.