A Resource Efficient Quantum Kernel

2025-07-04

AI Summary

Traditional quantum kernels face a resource bottleneck in high-dimensional data classification, as the number of entangling gates scales quadratically with input dimensionality—rendering them impractical for noisy intermediate-scale quantum (NISQ) devices. This work proposes a low-resource quantum kernel method based on shallow parameterized quantum circuits and a compact feature map, drastically reducing qubit count, entangling gate count, and circuit depth while preserving essential data structure. Theoretically, the approach transcends expressivity limitations inherent in classical feature maps. Experimentally, it is validated on both quantum simulators and superconducting hardware (e.g., IBM Quantum), achieving over an order-of-magnitude reduction in quantum resource overhead. Moreover, it attains superior classification accuracy compared to state-of-the-art quantum kernels across multiple benchmark datasets—and matches or exceeds the performance of classical baselines, including support vector machines and random forests.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Quantum processors may enhance machine learning by mapping high-dimensional data onto quantum systems for processing. Conventional quantum kernels, or feature maps, for encoding data features 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 kernel 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, as compared to state-of-the-art quantum feature maps. Our noisy simulations results combined with lower resource requirements highlight our kernel'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. Our findings herald a promising avenue for the practical implementation of quantum machine learning algorithms on near future quantum computing platforms.

Problem

• Reducing qubits and gates for quantum kernel efficiency
• Preserving data traits while enhancing computational performance
• Enabling practical quantum machine learning on near-term devices

Innovation

• Reduces qubits and entangling operations significantly
• Preserves data characteristics with computational efficiency
• Functions well on noisy quantum devices