HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks

• URL: http://arxiv.org/abs/2506.21161v2
• Date: Mon, 14 Jul 2025 01:58:33 GMT
• Title: HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks
• Authors: Yuxiang Liu, Fanxu Meng, Lu Wang, Yi Hu, Sixuan Li, Xutao Yu, Zaichen Zhang,
• Abstract summary: HaQGNN is a hardware-aware quantum kernel design method that integrates quantum device topology, noise characteristics, and Graph Neural Networks (GNNs)<n>Our results highlight the potential of learning-based and hardware-aware strategies for advancing practical quantum kernel design on near-term quantum hardware.
• Score: 18.080290351942736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing effective quantum kernels is a central challenge in Quantum Machine Learning (QML), particularly under the limitations of Noisy Intermediate-Scale Quantum (NISQ) devices with a limited number of qubits, error-prone gate operations, and restricted qubit connectivity. To address this, we propose HaQGNN, a hardware-aware quantum kernel design method that integrates quantum device topology, noise characteristics, and Graph Neural Networks (GNNs) to evaluate and select task-relevant quantum circuits that define quantum kernels. First, each quantum circuit is represented as a directed acyclic graph that encodes hardware-specific features, including gate types, target qubits, and noise characteristics. Next, two GNNs are trained to predict surrogate metrics, Probability of Successful Trials (PST) and Kernel-Target Alignment (KTA), for fast and accurate fidelity and performance estimation. Additionally, feature selection is further incorporated to reduce input dimensionality and improve compatibility with limited-qubit devices. Finally, extensive experiments on three benchmark datasets, Credit Card (CC), MNIST-5, and FMNIST-4, demonstrate that HaQGNN outperforms existing baselines in terms of classification accuracy. Our results highlight the potential of learning-based and hardware-aware strategies for advancing practical quantum kernel design on near-term quantum hardware.

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