Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demo…
Anomaly DetectionBenchmarkingQuantum Machine LearningQuantum deep recurrent reinforcement learning
Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can …
Decision MakingQ-LearningQuantum Machine Learningreinforcement-learning+3Towards Quantum-Enabled 6G Slicing
The quantum machine learning (QML) paradigms and their synergies with network slicing can be envisioned to be a disruptive technology on the cusp of entering to era of sixth-generation (6G), where the mobile communicatio…
Deep Reinforcement LearningFederated LearningQuantum Machine Learningreinforcement-learning+1Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer …
Quantum Machine LearningTheory for Equivariant Quantum Neural Networks
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this …
Quantum Machine LearningRepresentation Theory for Geometric Quantum Machine Learning
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importation of these ideas, combined with an exist…
Quantum Machine LearningAn efficient combination strategy for hybird quantum ensemble classifier
Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability f…
Ensemble LearningQuantum Machine LearningQuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push fo…
Quantum Machine LearningA kernel-based quantum random forest for improved classification
The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative to develop quantum models with unique mo…
General ClassificationQuantum Machine LearningquEEGNet: Quantum AI for Biosignal Processing
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural…
EEGElectroencephalogram (EEG)Quantum Machine LearningParameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach
Quantum machine learning (QML) is the use of quantum computing for the computation of machine learning algorithms. With the prevalence and importance of classical data, a hybrid quantum-classical approach to QML is calle…
Dimensionality ReductionQuantum Machine LearningTopological data analysis on noisy quantum computers
Topological data analysis (TDA) is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data. However, the computational demands of classical algorithms for computing TDA a…
Quantum Machine LearningTopological Data AnalysisThe role of entanglement for enhancing the efficiency of quantum kernels towards classification
Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, i…
Quantum Machine LearningQuantum-Classical Hybrid Information Processing via a Single Quantum System
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologies are restricted to a single classical o…
Quantum Machine LearningA preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms
Quantum Machine Learning (QML) hasn't yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniq…
Dimensionality ReductionQuantum Machine LearningExponential concentration in quantum kernel methods
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a …
Quantum Machine LearningQuantum Multi-Agent Meta Reinforcement Learning
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by thi…
Meta-LearningMeta Reinforcement LearningMulti-agent Reinforcement LearningQuantum Machine Learning+3Quantum Bayesian Computation
Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computation. Our paper adds to the literature in…
feature selectionGaussian ProcessesQuantum Machine LearningHeart Disease Detection using Quantum Computing and Partitioned Random Forest Methods
Heart disease morbidity and mortality rates are increasing, which has a negative impact on public health and the global economy. Early detection of heart disease reduces the incidence of heart mortality and morbidity. Re…
ClassificationDisease PredictionQuantum Machine LearningMixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computer…
feature selectionFraud DetectionQuantum Machine Learning