A Resource Efficient Quantum Kernel

TL;DR

This paper introduces a resource-efficient quantum feature map that reduces the number of qubits and entangling gates needed, enabling practical quantum machine learning with high-dimensional data on noisy intermediate-scale quantum devices.

Contribution

The authors propose a novel quantum feature map that maintains data characteristics while significantly lowering resource requirements, improving upon existing quantum feature maps.

Findings

Improved accuracy and resource efficiency demonstrated on benchmark datasets.

Effective performance within noisy quantum device constraints.

Comparable or superior classification results to classical algorithms.

Abstract

Quantum processors may enhance machine learning by mapping high-dimensional data onto quantum systems for processing. Conventional feature maps, for encoding data onto a quantum circuit are currently impractical, as the number of entangling gates scales quadratically with the dimension of the dataset and the number of qubits. In this work, we introduce a quantum feature map designed to handle high-dimensional data with a significantly reduced number of qubits and entangling operations. Our approach preserves essential data characteristics while promoting computational efficiency, as evidenced by extensive experiments on benchmark datasets that demonstrate a marked improvement in both accuracy and resource utilization when using our feature map as a kernel for characterization, as compared to state-of-the-art quantum feature maps. Our noisy simulation results, combined with lower resource requirements, highlight our map's ability to function within the constraints of noisy intermediate-scale quantum devices. Through numerical simulations and small-scale implementation on a superconducting circuit quantum computing platform, we demonstrate that our scheme performs on par or better than a set of classical algorithms for classification. While quantum kernels are typically stymied by exponential concentration, our approach is affected with a slower rate with respect to both the number of qubits and features, which allows practical applications to remain within reach. Our findings herald a promising avenue for the practical implementation of quantum machine learning algorithms on near future quantum computing platforms.