Abstract: Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low ...

Abstract: This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by ...

Quantum machine learning integrates principles from quantum mechanics and classical learning to probe new frontiers in computational capability. By harnessing superposition and entanglement, quantum ...

Tensor network methods provide a structured approach to representing and manipulating high-dimensional data by decomposing global information into interconnected low-rank tensors. Originating in the ...

This project implements and rigorously evaluates a quantum-classical hybrid machine learning pipeline to predict HOMO energies (Highest Occupied Molecular Orbital) of organic molecules from the QM9 ...

Meta has been one of the most interesting companies of the generative AI era — initially gaining a loyal and huge following of users for the release of its mostly open source Llama family of large ...

Quantum computing firm Xanadu has launched a new research initiative with defense giant Lockheed Martin to push the boundaries of Quantum Machine Learning, or QML. The partnership will explore whether ...

Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from massive datasets and powering next-gen AI. That future might be closer than ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

Telstra has completed a trial with Silicon Quantum Computing (SQC) that sought to apply quantum machine learning to boost network automation. The 12-month trial saw the pair leverage Watermelon, SQC’s ...