Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload
The rapid advancement of artificial intelligence (AI) and deep learning (DL) has catalyzed the emergence of several optimization-driven subfields, notably neuromorphic computing and quantum machine learning. Leveraging t…
Quantum Machine LearningQuantum feature encoding optimization
Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of input data, which plays a pivotal role i…
Quantum Machine LearningGenerative modeling using evolved quantum Boltzmann machines
Born-rule generative modeling, a central task in quantum machine learning, seeks to learn probability distributions that can be efficiently sampled by measuring complex quantum states. One hope is for quantum models to e…
Quantum Machine LearningPerformance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris an…
Quantum Machine LearningLearning Reduced Representations for Quantum Classifiers
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this impasse is the application of dimensionali…
Quantum Machine LearningDimensionality ReductionBinary ClassificationFragmentation is Efficiently Learnable by Quantum Neural Networks
In certain classes of physical quantum systems, the exponentially large state space "fragments" into many low-dimensional, dynamically disconnected subspaces. We introduce a learning problem known as fragment classificat…
Quantum Machine LearningQuantum Machine Learning for Secondary Frequency Control
Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and sc…
Quantum Machine LearningQSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks
Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness…
Quantum Machine LearningParallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification
Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Clas…
Medical Image ClassificationQuantum Machine LearningBinary ClassificationRELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. …
Quantum Machine LearningReinforcement LearningGraph Neural NetworkModeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment…
Quantum Machine LearningIntrusion DetectionAnomaly DetectionReadout-Side Bypass for Residual Hybrid Quantum-Classical Models
Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits performance and amplifies privacy risk. We p…
Quantum Machine LearningNeural Architecture Search for Quantum Autoencoders
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data.…
Neural Architecture SearchQuantum Machine LearningImage ClassificationSpeech RecognitionAdaptive Conformal Prediction for Quantum Machine Learning
Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain underdeveloped in the quantum domain, despit…
Quantum Machine LearningA Primer on Quantum Machine Learning
Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle opti…
Quantum Machine LearningReinforcement LearningQML-HCS: A Hypercausal Quantum Machine Learning Framework for Non-Stationary Environments
QML-HCS is a research-grade framework for constructing and analyzing quantum-inspired machine learning models operating under hypercausal feedback dynamics. Hypercausal refers to AI systems that leverage extended, deep, …
Quantum Machine LearningQuantum Machine Learning via Contrastive Training
Quantum machine learning (QML) has attracted growing interest with the rapid parallel advances in large-scale classical machine learning and quantum technologies. Similar to classical machine learning, QML models also fa…
Quantum Machine LearningRepresentation LearningLeveraging Quantum-Based Architectures for Robust Diagnostics
Quantum machine learning has emerged as a promising approach for medical image analysis, particularly in settings where compact models and expressive feature representations are desired. This paper presents a hybrid clas…
Medical Image ClassificationBrain Tumor ClassificationQuantum Machine LearningTransfer LearningQuantum Artificial Intelligence for Mission-Critical Systems: Foundations, Architectural Elements, and Future Directions
Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the clas…
Quantum Machine LearningDecision MakingA Versatile Variational Quantum Kernel Framework for Non-Trivial Classification
Quantum kernel methods are a promising branch of quantum machine learning, yet their effectiveness on diverse, high-dimensional, real-world data remains unverified. Current research has largely been limited to low-dimens…
Quantum Machine Learning