AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work provides a pragmatic, resource-efficient blueprint for implementing core machine learning primitives on near-term quantum devices by proposing a qubit-efficient, hybrid quantum-classical algorithm that mitigates limitations of NISQ-era hardware.
It is explained that the efficiently preparable states, device-generated distributions, variationally learned loading, and amortized preparation are required to get advantage from quantum machine learning and close with a checklist for evaluating input-dependent advantage claims.
Selecting a data encoding is a central and poorly tooled decision in quantum machine learning. The feature map fixes the geometry of the Hilbert space, the expressibility of quantum kernels, and whether the circuit can run on near-term hardware. This paper presents Quantum Encoding Agents, an open-source system that tu...
Quantumer, a hybrid TinyML--quantum framework that integrates multi-scale dilated convolutions and scaled dot-product attention within a lightweight transformer architecture, employing a two-stage transfer learning pipeline from Quantum Pre-Training (Quantumer-Q) to Classical Fine-Tuning (Quantumer-C).
An experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion is reported, which highlights the potential of qudits to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices.
Encoding classical data into quantum systems is a foundational step in the execution of nearly all quantum algorithms, and a critical bottleneck in realizing practical quantum advantage. This review provides a comprehensive account of the concepts, algorithms, and practical considerations associated with quantum data e...
This work proposes Quantum SEDONet (Spectral-Embedded Deep Operator Network), which assigns each trunk coordinate a spectral basis according to its boundary condition: Fourier features for periodic coordinates and Chebyshev features for bounded, non-periodic coordinates.
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