Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model
Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training -- such as collecting relevant samples, labeling data, and consuming computing power -- model-steali…
Neural Architecture SearchQuantum Machine LearningShot Optimization in Quantum Machine Learning Architectures to Accelerate Training
In this paper, we propose shot optimization method for QML models at the expense of minimal impact on model performance. We use classification task as a test case for MNIST and FMNIST datasets using a hybrid quantum-clas…
Quantum Machine LearningQuantum machine learning for image classification
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learnin…
ClassificationEfficient Neural Networkimage-classificationImage Classification+2Learning To Optimize Quantum Neural Network Without Gradients
Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain h…
Quantum Machine LearningImproving Gradient Methods via Coordinate Transformations: Applications to Quantum Machine Learning
Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overall performance is dependent on the appea…
Quantum Machine LearningRobust Dequantization of the Quantum Singular value Transformation and Quantum Machine Learning Algorithms
Several quantum algorithms for linear algebra problems, and in particular quantum machine learning problems, have been "dequantized" in the past few years. These dequantization results typically hold when classical algor…
Quantum Machine LearningRecommendation SystemsQuantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees. Quantum models implement implicit probabilistic predictors that produce multiple…
Conformal PredictionQuantum Machine LearningUncertainty QuantificationModel-Driven Quantum Federated Learning (QFL)
Recently, several studies have proposed frameworks for Quantum Federated Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. Ho…
Federated LearningmodelPositionQuantum Machine LearningQuantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerf…
Deep Reinforcement LearningQuantum Machine Learningreinforcement-learningReinforcement LearningTetra-AML: Automatic Machine Learning via Tensor Networks
Neural networks have revolutionized many aspects of society but in the era of huge models with billions of parameters, optimizing and deploying them for commercial applications can require significant computational and f…
Bayesian OptimizationHyperparameter OptimizationModel CompressionNeural Architecture Search+3A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build ove…
Quantum Machine LearningGeneralization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
Generalization is the ability of machine learning models to make accurate predictions on new data by learning from training data. However, understanding generalization of quantum machine learning models has been a major …
Out-of-Distribution GeneralizationQuantum Machine LearningA Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification
In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particula…
Quantum Machine LearningTensor networks for quantum machine learning
Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning t…
Quantum Machine LearningTensor NetworksMAQA: A Quantum Framework for Supervised Learning
Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. However, the practical advantages of using…
DescriptiveQuantum Machine LearningChallenges and Opportunities in Quantum Machine Learning
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochem…
Quantum Machine LearningAutomated Vulnerability Detection in Source Code Using Quantum Natural Language Processing
One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, o…
Quantum Machine LearningVulnerability DetectionQuantum Machine Learning Implementations: Proposals and Experiments
This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews sp…
Quantum Machine LearningVariational Quantum Neural Networks (VQNNS) in Image Classification
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are abl…
Classificationimage-classificationImage ClassificationQuantum Machine LearningThe Robustness Verification of Linear Sound Quantum Classifiers
I present a quick and sound method for the robustness verification of a sort of quantum classifiers who are Linear Sound. Since quantum machine learning has been put into practice in relevant fields and Linear Sound Prop…
Quantum Machine Learning