Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications
Speech-based machine learning systems are sensitive to noise, complicating reliable deployment in emotion recognition and voice pathology detection. We evaluate the robustness of a hybrid quantum machine learning model, …
Voice pathology detectionQuantum Machine LearningEmotion Recognitionhdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures
Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also know…
Quantum Machine LearningGeometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is increasingly explored for biomedical pre…
Quantum Machine LearningQuantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model
Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of th…
Word Sense DisambiguationQuantum Machine LearningImage MatchingOne-Shot Structured Pruning of Quantum Neural Networks via $q$-Group Engineering and Quantum Geometric Metrics
Quantum neural networks (QNNs) suffer from severe gate-level redundancy, which hinders their deployment on noisy intermediate-scale quantum (NISQ) devices. In this work, we propose q-iPrune, a one-shot structured pruning…
Quantum Machine LearningQuantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems
Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to dete…
Quantum Machine LearningBinary ClassificationIntrusion DetectionQuantum Generative Models for Computational Fluid Dynamics: A First Exploration of Latent Space Learning in Lattice Boltzmann Simulations
This paper presents the first application of quantum generative models to learned latent space representations of computational fluid dynamics (CFD) data. While recent work has explored quantum models for learning statis…
Quantum Machine LearningHybrid Quantum-Classical Mixture of Experts: Unlocking Topological Advantage via Interference-Based Routing
The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of cl…
Quantum Machine LearningFederated LearningMachine Unlearning in the Era of Quantum Machine Learning: An Empirical Study
We present the first empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks. While MU has been extensively explored in classical deep learning, its behavior within variational quantum circ…
Quantum Machine LearningDomain-Aware Quantum Circuit for QML
Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices. W…
Quantum Machine LearningImage ClassificationQ-RUN: Quantum-Inspired Data Re-uploading Networks
Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks and have been shown to outperform classical neural networks in fitting high-frequency functions. However, their practic…
Quantum Machine LearningQuantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rend…
Quantum Machine LearningAnomaly DetectionBlack-Box Auditing of Quantum Model: Lifted Differential Privacy with Quantum Canaries
Quantum machine learning (QML) promises significant computational advantages, yet models trained on sensitive data risk memorizing individual records, creating serious privacy vulnerabilities. While Quantum Differential …
Quantum Machine LearningMaritime object classification with SAR imagery using quantum kernel methods
Illegal, unreported, and unregulated (IUU) fishing causes global economic losses of 10-25 billion USD annually and undermines marine sustainability and governance. Synthetic Aperture Radar (SAR) provides reliable maritim…
Quantum Machine LearningBinary ClassificationFRQI Pairs method for image classification using Quantum Recurrent Neural Network
This study aims to introduce the FRQI Pairs method to a wider audience, a novel approach to image classification using Quantum Recurrent Neural Networks (QRNN) with Flexible Representation for Quantum Images (FRQI). The …
Quantum Machine LearningImage ClassificationLiePrune: Lie Group and Quantum Geometric Dual Representation for One-Shot Structured Pruning of Quantum Neural Networks
Quantum neural networks (QNNs) and parameterized quantum circuits (PQCs) are key building blocks for near-term quantum machine learning. However, their scalability is constrained by excessive parameters, barren plateaus,…
Quantum Machine LearningGraph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variational quantum circuits (VQCs) using graph-…
Quantum Machine LearningGraph Neural NetworkQuantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle…
Quantum Machine LearningStock Market PredictionPERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning
In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyram…
Quantum Machine LearningGraph EmbeddingQuantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
We propose a novel QTGNN framework for detecting fraudulent transactions in large-scale financial networks. By integrating quantum embedding, variational graph convolutions, and topological data analysis, QTGNN captures …
Quantum Machine LearningFraud Detection