Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research foc…
Quantum Machine LearningQFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for …
Federated LearningFraud DetectionQuantum Machine LearningOptimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due t…
Quantum Machine LearningOn Optimizing Hyperparameters for Quantum Neural Networks
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, whe…
Quantum Machine LearningQuantum Algorithms: A New Frontier in Financial Crime Prevention
Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highl…
ManagementQuantum Machine LearningImage Classification with Rotation-Invariant Variational Quantum Circuits
Variational quantum algorithms are gaining attention as an early application of Noisy Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational methods lies in the phenomenon of Barren Plateaus, …
Classificationimage-classificationImage ClassificationInductive Bias+1FedQNN: Federated Learning using Quantum Neural Networks
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequen…
Federated LearningQuantum Machine LearningEarly-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quan…
Functional ConnectivityHippocampusQuantum Machine LearningTime SeriesEmpowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast dat…
Quantum Machine LearningBetter than classical? The subtle art of benchmarking quantum machine learning models
Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available. However, the huge impact of the experimental design on the results…
BenchmarkingBinary ClassificationDecision MakingExperimental Design+1Jet Discrimination with Quantum Complete Graph Neural Network
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum comput…
Graph Neural NetworkQuantum Machine LearningQubit-Wise Architecture Search Method for Variational Quantum Circuits
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural archi…
Evolutionary AlgorithmsNeural Architecture SearchQuantum Machine LearningParameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations
Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quan…
Quantum Machine LearningQuantum Mixed-State Self-Attention Network
Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Networ…
Quantum Machine Learningtext-classificationText ClassificationEEG-based Dementia Classification Using CS-EMD Synchrony Features and Quantum Machine Learning
Dementia is a condition that affects a very large population, and an effective clinical diagnosis can lead to early intervention to delay progressive cognitive decline. A relatively low-cost, noninvasive, and highly a…
EEGQuantum Machine LearningSpecificityClassification of the Fashion-MNIST Dataset on a Quantum Computer
The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quantum computers are not only too costly fo…
BenchmarkingQuantum Machine LearningLearning to Program Variational Quantum Circuits with Fast Weights
Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages notably within domains such as Reinforc…
Quantum Machine LearningReinforcement Learning (RL)Time SeriesTime Series PredictionStreaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics…
Distributed ComputingEdge-computingQuantum Machine LearningA Quick Introduction to Quantum Machine Learning for Non-Practitioners
This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum …
Quantum Machine LearningSpectral invariance and maximality properties of the frequency spectrum of quantum neural networks
Quantum Neural Networks (QNNs) are a popular approach in Quantum Machine Learning. We analyze this frequency spectrum using the Minkowski sum for sets and the set of differences, which makes it particularly easy to expre…
Quantum Machine Learning