Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications
Within quantum machine learning, parametrized quantum circuits provide flexible quantum models, but their performance is often highly task-dependent, making manual circuit design challenging. Alternatively, quantum archi…
Quantum Machine LearningMerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, d…
Quantum Machine LearningSAQNN: Spectral Adaptive Quantum Neural Network as a Universal Approximator
Quantum machine learning (QML), as an interdisciplinary field bridging quantum computing and machine learning, has garnered significant attention in recent years. Currently, the field as a whole faces challenges due to i…
Quantum Machine LearningEmpirical Study of Observable Sets in Multiclass Quantum Classification
Variational quantum algorithms have gained attention as early applications of quantum computers for learning tasks. In the context of supervised learning, most of the works that tackle classification problems with parame…
Quantum Machine LearningBinary ClassificationHyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance
Group-equivariant quantum machine learning has emerged as a promising paradigm by incorporating symmetry into quantum models. However, constructing models simultaneously equivariant to both rotational and permutational s…
3D Point Cloud ClassificationQuantum Machine LearningQ-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
Quantum Machine Learning (QML) promises significant computational advantages, but preserving training data privacy remains challenging. Classical approaches like differentially private stochastic gradient descent (DP-SGD…
Quantum Machine LearningInvestigating Quantum Circuit Designs Using Neuro-Evolution
Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manu…
Quantum Machine LearningQuantum Generator Kernels
Quantum kernel methods offer significant theoretical benefits by rendering classically inseparable features separable in quantum space. Yet, the practical application of Quantum Machine Learning (QML), currently constrai…
Quantum Machine LearningMultitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the …
Quantum Machine LearningHierarchy of discriminative power and complexity in learning quantum ensembles
Distance metrics are central to machine learning, yet distances between ensembles of quantum states remain poorly understood due to fundamental quantum measurement constraints. We introduce a hierarchy of integral probab…
Quantum Machine LearningUniversality of Many-body Projected Ensemble for Learning Quantum Data Distribution
Generating quantum data by learning the underlying quantum distribution poses challenges in both theoretical and practical scenarios, yet it is a critical task for understanding quantum systems. A fundamental question in…
Quantum Machine LearningAnalyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia
This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world medical imaging despite operating under severe constraints. We evaluate E…
Quantum Machine LearningBinary ClassificationLightweight Quantum-Enhanced ResNet for Coronary Angiography Classification: A Hybrid Quantum-Classical Feature Enhancement Framework
Background: Coronary angiography (CAG) is the cornerstone imaging modality for evaluating coronary artery stenosis and guiding interventional decision-making. However, interpretation based on single-frame angiographic im…
Quantum Machine LearningBinary ClassificationQuantum Super-resolution by Adaptive Non-local Observables
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large …
Quantum Machine LearningQuantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced compu…
Quantum Machine LearningImpact of Circuit Depth versus Qubit Count on Variational Quantum Classifiers for Higgs Boson Signal Detection
High-Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), generate massive datasets that challenge classical computational limits. Quantum Machine Learning (QML) offers a potential advantag…
Quantum Machine LearningDimensionality ReductionAnomaly DetectionThe Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although nu…
Quantum Machine LearningQNeRF: Neural Radiance Fields on a Simulated Gate-Based Quantum Computer
Recently, Quantum Visual Fields (QVFs) have shown promising improvements in model compactness and convergence speed for learning the provided 2D or 3D signals. Meanwhile, novel-view synthesis has seen major advances with…
Quantum Machine LearningRepresentation LearningDistribution-Guided and Constrained Quantum Machine Unlearning
Machine unlearning aims to remove the influence of specific training data from a learned model without full retraining. While recent work has begun to explore unlearning in quantum machine learning, existing approaches l…
Quantum Machine LearningShallow-circuit Supervised Learning on a Quantum Processor
Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of class…
Quantum Machine Learning