This systematic review explores the integration of 4D/3D printing technologies with machine
learning, shaping a new era of manufacturing innovation. The analysis covers a wide range …

This article examines the transformative potential of quantum computing in addressing the
growing challenge of cyber threats. With traditional encryption methods becoming …

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …

Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning
Models Page 1 International Journal of Advanced Engineering Research and Science (IJAERS) …

We present a classical algorithm for estimating expectation values of arbitrary observables
on most quantum circuits across all circuit architectures and depths, including those with all …

Generalization is the ability of machine learning models to make accurate predictions on
new data by learning from training data. However, understanding generalization of quantum …

Learning tasks play an increasingly prominent role in quantum information and computation.
They range from fundamental problems such as state discrimination and metrology over the …

Quantum machine learning (QML) shows promise for analyzing quantum data. A notable
example is the use of quantum convolutional neural networks (QCNNs), implemented as …

Self-attention mechanism (SAM) is good at capturing the intrinsic connection between
features to dramatically boost the performance of machine learning models. Nevertheless …

Adversarial robustness and generalization are both crucial properties of reliable machine
learning models. In this paper, we study these properties in the context of quantum machine …