Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes
Symmetry is a strong inductive bias in geometric deep learning and its quantum counterpart, and has attracted increasing attention for improving the trainability of QML models. Yet incorporating symmetries into quantum m…
Quantum Machine LearningInfluence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models
Data dimensionality reduction techniques are often utilized in the implementation of Quantum Machine Learning models to address two significant issues: the constraints of NISQ quantum devices, which are characterized by …
Quantum Machine LearningDimensionality ReductionTrustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hy…
Quantum Machine LearningAdversarial RobustnessDecision MakingQuantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection
Anomaly detection in time-series data is a critical challenge with significant implications for network security. Recent quantum machine learning approaches, such as quantum kernel methods and variational quantum circuit…
Quantum Machine LearningAnomaly DetectionA Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adversarial framework that integrates a hybri…
Quantum Machine LearningQuantum Phase Classification of Rydberg Atom Systems Using Resource-Efficient Variational Quantum Circuits and Classical Shadows
Quantum phase transitions in Rydberg atom arrays present significant opportunities for studying many-body physics, yet distinguishing between different ordered phases without explicit order parameters remains challenging…
Quantum Machine LearningDimensionality ReductionQuantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine
Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches, as computational techniques are key to improving the performance of image classification tas…
Dimensionality ReductionQuantum Machine LearningImage ClassificationFIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in g…
Quantum Machine LearningReinforcement LearningIQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers n…
Quantum Machine LearningAdapting Quantum Machine Learning for Energy Dissociation of Bonds
Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical m…
Molecular Property PredictionQuantum Machine LearningQuantum Computing Methods for Malware Detection
In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum S…
Quantum Machine LearningMalware DetectionFisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective …
Quantum Machine LearningQuantum generative model on bicycle-sharing system and an application
Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads to bicycle shortages in specific areas …
Quantum Machine LearningThe Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
Recent advancements in QML and SNNs have generated considerable excitement, promising exponential speedups and brain-like energy efficiency to revolutionize AI. However, this paper argues that they are unlikely to displa…
Quantum Machine LearningQuantum feature-map learning with reduced resource overhead
Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We int…
Quantum Machine LearningLayerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for distributed QML in a quantum federated learn…
Quantum Machine LearningFederated LearningQuantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its dispersive limit (DJC). These models provide high-dimensional Hilbert spaces an…
Quantum Machine LearningMulti-channel convolutional neural quantum embedding
Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational quantum circuits involves embedding the…
Quantum Machine LearningComprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations
This paper presents a systematic comparative analysis of Variational Quantum Classifier (VQC) configurations for financial fraud detection, encompassing three distinct quantum encoding techniques and comprehensive archit…
Quantum Machine LearningComputational EfficiencyFraud DetectionTowards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey
This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS), with particular emphasis on deep learning and quantum machine learning approaches. FL enables collaborative…
Network Intrusion DetectionQuantum Machine LearningFederated LearningModel Compression