Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification
In recent years, quantum machine learning has emerged as a promising intersection between quantum physics and artificial intelligence, particularly in domains requiring advanced pattern recognition such as healthcare. Th…
Cancer ClassificationDiagnosticLung Cancer DiagnosisQuantum Machine Learning+1Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Variational Quantum Algorithms (VQAs) are a promising tool in the NISQ era, leveraging quantum computing across diverse fields. However, their performance is hindered by optimization challenges like local minima, barren …
NavigateQuantum Machine LearningSupervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent…
Quantum Machine LearningQGAN-based data augmentation for hybrid quantum-classical neural networks
Quantum neural networks converge faster and achieve higher accuracy than classical models. However, data augmentation in quantum machine learning remains underexplored. To tackle data scarcity, we integrate quantum gener…
Data AugmentationQuantum Machine LearningTensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum…
Anomaly DetectionQuantum Machine LearningTensor NetworksQuantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models
Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic met…
DiagnosticQuantum Machine LearningLeveraging Diffusion Models for Parameterized Quantum Circuit Generation
Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusi…
Computational EfficiencyDenoisingQuantum Circuit GenerationQuantum Machine LearningPredictive Performance of Deep Quantum Data Re-uploading Models
Quantum machine learning models incorporating data re-uploading circuits have garnered significant attention due to their exceptional expressivity and trainability. However, their ability to generate accurate predictions…
Quantum Machine LearningQiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primitives to facilitate interactions with cl…
Quantum Machine LearningExperimental robustness benchmark of quantum neural network on a superconducting quantum processor
Quantum machine learning (QML) models, like their classical counterparts, are vulnerable to adversarial attacks, hindering their secure deployment. Here, we report the first systematic experimental robustness benchmark f…
Adversarial AttackAdversarial RobustnessBenchmarkingQuantum Machine LearningBenchmarking data encoding methods in Quantum Machine Learning
Data encoding plays a fundamental and distinctive role in Quantum Machine Learning (QML). While classical approaches process data directly as vectors, QML may require transforming classical data into quantum states throu…
BenchmarkingQuantum Machine LearningLearning to Program Quantum Measurements for Machine Learning
The rapid advancements in quantum computing (QC) and machine learning (ML) have sparked significant interest, driving extensive exploration of quantum machine learning (QML) algorithms to address a wide range of complex …
Quantum Machine LearningQuXAI: Explainers for Hybrid Quantum Machine Learning Models
The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparen…
Feature ImportanceQuantum Machine LearningQuantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requirements of deep learning models. Quantum-Tra…
image-classificationImage ClassificationLanguage ModelingLanguage Modelling+4Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
We introduce a distributed quantum-classical framework that synergizes photonic quantum neural networks (QNNs) with matrix-product-state (MPS) mapping to achieve parameter-efficient training of classical neural networks.…
Quantum Machine LearningAddressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a multi-chip ensemble VQC framework that …
EEGQuantum Machine LearningDifferentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation
The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational qua…
Quantum Machine LearningTime Series PredictionHMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems
Quantum machine learning for spin and molecular systems faces critical challenges of scarce labeled data and computationally expensive simulations. To address these limitations, we introduce Hamiltonian-Masked Autoencodi…
Few-Shot LearningQuantum Machine LearningTensor NetworksTransfer LearningToward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection
We present a novel hybrid quantum-classical neural network architecture for fraud detection that integrates a classical Long Short-Term Memory (LSTM) network with a variational quantum circuit. By leveraging quantum phen…
Fraud DetectionQuantum Machine LearningQuantum Autoencoder for Multivariate Time Series Anomaly Detection
Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurati…
Anomaly DetectionData CompressionQuantum Machine LearningTime Series+1