Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Impact of Data Augmentation on QCNNs
In recent years, Classical Convolutional Neural Networks (CNNs) have been applied for image recognition successfully. Quantum Convolutional Neural Networks (QCNNs) are proposed as a novel generalization to CNNs by using …
Data AugmentationLearning TheoryQuantum Machine LearningQuantum Kernel t-Distributed Stochastic Neighbor Embedding
Data visualization is important in understanding the characteristics of data that are difficult to see directly. It is used to visualize loss landscapes and optimization trajectories to analyze optimization performance. …
Data VisualizationQuantum Machine LearningQuantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries. However, similarly to…
Adversarial RobustnessMulti-class ClassificationQuantum Machine LearningA novel feature selection method based on quantum support vector machine
Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguo…
feature selectionQuantum Machine LearningTowards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural …
ClassificationComputed Tomography (CT)image-classificationImage Classification+2Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
In the race towards quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. However, Noisy Intermediate-Scale Quantum (NISQ) processors are prone to errors, which po…
Quantum Machine LearningBridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation
Quantum neural networks (QNNs), harnessing superposition and entanglement, have shown potential to surpass classical methods in complex learning tasks but remain limited by hardware constraints and noisy conditions. In t…
Dimensionality ReductionImage ClassificationKnowledge DistillationQuantum Machine Learning+1Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration
In this study, we introduce an innovative Quantum-enhanced Support Vector Machine (QSVM) approach for stellar classification, leveraging the power of quantum computing and GPU acceleration. Our QSVM algorithm significant…
ClassificationComputational EfficiencyGPUQuantum Machine LearningTraining robust and generalizable quantum models
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 learning based on Lipschitz bounds. We…
Adversarial RobustnessQuantum Machine LearningSymmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making us…
Atomic ForcesQuantum Machine LearningQuantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing
Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, withi…
Quantum Machine LearningsQUlearn -- A Python Library for Quantum Machine Learning
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer archi…
Quantum Machine LearningQuantum Machine Learning for Remote Sensing: Exploring potential and challenges
The industry of quantum technologies is rapidly expanding, offering promising opportunities for various scientific domains. Among these emerging technologies, Quantum Machine Learning (QML) has attracted considerable att…
Quantum Machine Learning3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds
Existing methods for learning 3D representations are deep neural networks trained and tested on classical hardware. Quantum machine learning architectures, despite their theoretically predicted advantages in terms of spe…
Quantum Machine LearningDisentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
Quantum computing offers the potential for superior computational capabilities, particularly for data-intensive tasks. However, the current state of quantum hardware puts heavy restrictions on input size. To address this…
Quantum Machine LearningTransfer LearningExpressibility-induced Concentration of Quantum Neural Tangent Kernels
Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial importance in designing appropriate circuit ar…
Quantum Machine LearningHybrid quantum image classification and federated learning for hepatic steatosis diagnosis
In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial. Recognizing the essential need for improved diagnostic precision, particularly for optimizing diagnosis time by swiftly h…
DiagnosticFederated Learningimage-classificationImage Classification+1Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of bounded gate complexity, it is natural t…
Quantum Machine LearningQuantum State TomographyA general learning scheme for classical and quantum Ising machines
An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learnin…
Quantum Machine LearningUnifying (Quantum) Statistical and Parametrized (Quantum) Algorithms
Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective for most classical machine learning algorithms. This ceases to be true in quantum learning, where many settings do not admit, neither an SQ ana…
Quantum Machine Learning