Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
On Quantum Perceptron Learning via Quantum Search
With the growing interest in quantum machine learning, the perceptron -- a fundamental building block in traditional machine learning -- has emerged as a valuable model for exploring quantum advantages. Two quantum perce…
Quantum Machine LearningEnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variable-length circuits that introduce high o…
Quantum Machine LearningSingle-Qudit Quantum Neural Networks for Multiclass Classification
This paper proposes a single-qudit quantum neural network for multiclass classification, by using the enhanced representational capacity of high-dimensional qudit states. Our design employs an $d$-dimensional unitary ope…
ClassificationQuantum Machine LearningOn the status of current quantum machine learning software
The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to study their application to real-life computational problems. However, hardware challenges are not the only ones that h…
Image SegmentationQuantum Machine LearningSemantic SegmentationQUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the …
Image EnhancementImage RestorationImage Super-ResolutionQuantum Machine Learning+2Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification
Quantum vision transformers (QViTs) build on vision transformers (ViTs) by replacing linear layers within the self-attention mechanism with parameterised quantum neural networks (QNNs), harnessing quantum mechanical prop…
image-classificationImage ClassificationKnowledge DistillationQuantum Machine LearningTransforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications
Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. Ho…
Quantum Machine LearningOn the similarity of bandwidth-tuned quantum kernels and classical kernels
Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains uncertain. This limitation can be attributed…
Quantum Machine LearningSeismic inversion using hybrid quantum neural networks
Quantum computing leverages qubits, exploiting superposition and entanglement to solve problems intractable for classical computers, offering significant computational advantages. Quantum machine learning (QML), which in…
DecoderQuantum Machine LearningSeismic InversionCQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI
The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs)…
Alzheimer's Disease DetectionQuantum Machine LearningPhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier
Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenar…
feature selectionQuantum Machine LearningQCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, a…
Classificationimbalanced classificationProperty PredictionQuantum Machine Learning+1AutoQML: A Framework for Automated Quantum Machine Learning
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervent…
AutoMLHyperparameter OptimizationQuantum Machine LearningExploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in decoding EEG signals, they often struggle wi…
EEGQuantum Machine LearningQuantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?
The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fa…
Automated Theorem ProvingDrug DiscoveryQuantum Machine LearningExpressive equivalence of classical and quantum restricted Boltzmann machines
Quantum computers offer the potential for efficiently sampling from complex probability distributions, attracting increasing interest in generative modeling within quantum machine learning. This surge in interest has dri…
Quantum Machine LearningQuantum autoencoders for image classification
Classical machine learning often struggles with complex, high-dimensional data. Quantum machine learning offers a potential solution, promising more efficient processing. The quantum convolutional neural network (QCNN), …
ClassificationData Compressionimage-classificationImage Classification+1Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classi…
Quantum Machine LearningregressionBenchmarking MedMNIST dataset on real quantum hardware
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML s…
Benchmarkingimage-classificationImage ClassificationMedical Image Classification+1Qubit-Based Framework for Quantum Machine Learning: Bridging Classical Data and Quantum Algorithms
This paper dives into the exciting and rapidly growing field of quantum computing, explaining its core ideas, current progress, and how it could revolutionize the way we solve complex problems. It starts by breaking down…
Quantum Machine Learning