Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Transition Role of Entangled Data in Quantum Machine Learning
Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measu…
PredictionQuantum Machine LearningCase Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address c…
Malware ClassificationQuantum Machine LearningExploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks …
Quantum Machine LearningShadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of qu…
Quantum Machine LearningSoftware Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software de…
Quantum Machine LearningImproved Financial Forecasting via Quantum Machine Learning
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we u…
Point ProcessesQuantum Machine LearningQuantum Convolutional Neural Networks for Multi-Channel Supervised Learning
As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly …
Quantum Machine LearningQuantum Text Classifier -- A Synchronistic Approach Towards Classical and Quantum Machine Learning
Although it will be a while before a practical quantum computer is available, there is no need to hold off. Methods and algorithms are being developed to demonstrate the feasibility of running machine learning (ML) pipel…
ClassificationQuantum Machine Learningtext-classificationText ClassificationOn quantum backpropagation, information reuse, and cheating measurement collapse
The success of modern deep learning hinges on the ability to train neural networks at scale. Through clever reuse of intermediate information, backpropagation facilitates training through gradient computation at a total …
Quantum Machine LearningSimulation of a Variational Quantum Perceptron using Grover's Algorithm
The quantum perceptron, the variational circuit, and the Grover algorithm have been proposed as promising components for quantum machine learning. This paper presents a new quantum perceptron that combines the quantum va…
Quantum Machine LearningPre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the …
Binary ClassificationQuantum Machine LearningTransfer LearningA Novel Stochastic LSTM Model Inspired by Quantum Machine Learning
Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent w…
Quantum Machine LearningVisual Question Answering (VQA)Implications of Deep Circuits in Improving Quality of Quantum Question Answering
Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well a…
Quantum Machine LearningQuestion AnsweringEnhancing Quantum Support Vector Machines through Variational Kernel Training
Quantum machine learning (QML) has witnessed immense progress recently, with quantum support vector machines (QSVMs) emerging as a promising model. This paper focuses on the two existing QSVM methods: quantum kernel SVM …
Quantum Machine LearningArchitectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge com…
Collaborative InferenceDistributed ComputingEdge-computingImage Classification+1Quantum Machine Learning for Malware Classification
In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malwa…
ClassificationMalware ClassificationQuantum Machine LearningEfficient information recovery from Pauli noise via classical shadow
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum c…
Quantum Machine LearningCan Feature Engineering Help Quantum Machine Learning for Malware Detection?
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are s…
Feature Engineeringfeature selectionMalware ClassificationMalware Detection+1An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits makes them a promising platform for demons…
Quantum Machine LearningVisual Question Answering (VQA)Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens
Surface roughness is a crucial factor influencing the performance and functionality of additive manufactured components. Accurate prediction of surface roughness is vital for optimizing manufacturing processes and ensuri…
Quantum Machine Learningregression