Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantum Embedding with Transformer for High-dimensional Data
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer …
Quantum Machine LearningEvaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we assess the efficacy of such attacks in the …
Quantum Machine LearningGuided Quantum Compression for High Dimensional Data Classification
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning appli…
Binary ClassificationClassificationDimensionality ReductionQuantum Machine LearningGenerating Universal Adversarial Perturbations for Quantum Classifiers
Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that,…
Quantum Machine LearningArbitrary Polynomial Separations in Trainable Quantum Machine Learning
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practic…
Quantum Machine LearningGeometric quantum machine learning of BQP$^A$ protocols and latent graph classifiers
Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used for constructing protocols with an expone…
Quantum Machine LearningUnleashing the Expressive Power of Pulse-Based Quantum Neural Networks
Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. While gate-based QML models are user-friendly for software engineers…
Quantum Machine LearningQSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering
The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at …
Earth ObservationQuantum Machine LearningSSIMCoherent Feed Forward Quantum Neural Network
Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requi…
BenchmarkingDiagnosticFraud DetectionQuantum Machine LearningAdversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train the quantum classifier by adopting an att…
Quantum Machine LearningBrain Tumor Diagnosis Using Quantum Convolutional Neural Networks
Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circ…
Computational EfficiencyDecision MakingQuantum Machine LearningGQHAN: A Grover-inspired Quantum Hard Attention Network
Numerous current Quantum Machine Learning (QML) models exhibit an inadequacy in discerning the significance of quantum data, resulting in diminished efficacy when handling extensive quantum datasets. Hard Attention Mecha…
Binary ClassificationHard AttentionQuantum Machine LearningA comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbia…
Learning TheoryQuantum Machine LearningNeural auto-designer for enhanced quantum kernels
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map. However, the challenge of d…
feature selectionQuantum Machine LearningA Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals
Autism Spectrum Disorder (ASD) significantly impacts social communication, interaction, and behavior. Early diagnosis and timely intervention can improve outcomes by enabling tailored therapeutic strategies. Electroencep…
Autism detectionDiagnosticEEGfeature selection+1Élivágar: Efficient Quantum Circuit Search for Classification
Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML cir…
ClassificationNeural Architecture SearchQuantum Machine LearningSymmetry breaking in geometric quantum machine learning in the presence of noise
Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouraging progress, the studies are still limit…
Quantum Machine LearningQuantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable f…
Federated LearningPrivacy PreservingQuantum Machine LearningQuantum Denoising Diffusion Models
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum …
DenoisingImage GenerationQuantum Machine LearningSSIMQuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving quantum advantages usually requires a la…
Quantum Machine Learning