AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Next-generation HQCNN architectures, including DMERA, HEA–QCNN, and (bQCNN) designs, significantly optimized for NISQ hardware are presented, establishing a scalable, parameter-efficient framework for high-performance hybrid quantum-classical ML on near-term devices.
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through e...
Findings show that current quantum feature maps through the encode–measure–boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.
A novel physics-guided linear mapper for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data, which reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms.
Modern High-Performance Computing (HPC) infrastructures are increasingly integrating multiple Quantum Processing Units (QPUs) of different types alongside classical resources. Unlike traditional accelerator scheduling, quantum device selection affects not only performance but also the quality of the computed result bec...
Findings establish QCBM as a viable complementary tool for data augmentation, particularly for low-dimensional structured tabular data with class imbalance, particularly for low-dimensional structured tabular data with class imbalance.
Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor.
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