Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are actively devising post-quantum solutions …
Intrusion DetectionNetwork Intrusion DetectionQuantum Machine LearningHyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters
Fine-tuning pre-trained large foundation models for specific tasks has become increasingly challenging due to the computational and storage demands associated with full parameter updates. Parameter-Efficient Fine-Tuning …
parameter-efficient fine-tuningQuantum Machine LearningApplication of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithm…
Binary ClassificationFeature EngineeringQuantum Machine LearningDetection of Physiological Data Tampering Attacks with Quantum Machine Learning
The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthcare systems which can be critical and li…
Data PoisoningQuantum Machine LearningQuantum automated learning with provable and explainable trainability
Machine learning is widely believed to be one of the most promising practical applications of quantum computing. Existing quantum machine learning schemes typically employ a quantum-classical hybrid approach that relies …
Quantum Machine LearningVariational decision diagrams for quantum-inspired machine learning applications
Decision diagrams (DDs) have emerged as an efficient tool for simulating quantum circuits due to their capacity to exploit data redundancies in quantum states and quantum operations, enabling the efficient computation of…
Quantum Machine LearningState EstimationImplementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's Pegasus quantum hardware to address dat…
Intrusion DetectionQuantum Machine LearningQuantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine l…
Quantum Machine LearningAdversarial Robustness of Partitioned Quantum Classifiers
Adversarial robustness in quantum classifiers is a critical area of study, providing insights into their performance compared to classical models and uncovering potential advantages inherent to quantum machine learning. …
Adversarial RobustnessQuantum Machine LearningHybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. Howeve…
Data CompressionManagementQuantum Machine LearningData re-uploading in Quantum Machine Learning for time series: application to traffic forecasting
Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban tran…
Quantum Machine LearningTime SeriesStability and Generalization of Quantum Neural Networks
Quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly growing field of quantum machine learning. While their empirical success is evident, the theoretical explorations of QNNs, pa…
Generalization BoundsLearning TheoryQuantum Machine LearningQuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks
We introduce Quantum Feature Extraction (QuFeX), a novel quantum machine learning module. The proposed module enables feature extraction in a reduced-dimensional space, significantly decreasing the number of parallel eva…
Autonomous DrivingImage SegmentationQuantum Machine LearningSemantic SegmentationRepresentation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
Quantum machine learning (QML) offers a promising avenue for advancing representation learning in complex signal domains. In this study, we investigate the use of parameterised quantum circuits (PQCs) for speech emotion …
Emotion ClassificationEmotion RecognitionQuantum Machine LearningRepresentation Learning+1Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm
Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of th…
Neural Architecture SearchQuantum Machine LearningDouble descent in quantum machine learning
The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behav…
Learning TheoryQuantum Machine LearningModeling Feature Maps for Quantum Machine Learning
Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This stu…
ClassificationQuantum Machine LearningModeling Quantum Machine Learning for Genomic Data Analysis
Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome seq…
Binary ClassificationQuantum Machine LearningLearning to Measure Quantum Neural Networks
The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into quantum machine learning (QML) algorithms to solve diverse and complex problems. D…
Quantum Machine LearningComparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
Purpose: Quantum computing promises to transform problem-solving across various domains with rapid and practical solutions. Within Software Evolution and Maintenance, Quantum Machine Learning (QML) remains mostly an unde…
Quantum Machine Learning