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Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications
Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data.
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.
Quantum Split Neural Network Learning using Cross-Channel Pooling
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing.
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
In modern on-driving computing environments, many sensors are used for context-aware applications.
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification
In recent years, quantum machine learning (QML) has been actively used for various tasks, e. g., classification, reinforcement learning, and adversarial learning.
3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN.
Scalable Quantum Convolutional Neural Networks
With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve.
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.
