Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification
Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-cla…
Audio ClassificationQuantum Machine LearningLorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, wit…
Graph Neural NetworkJet TaggingQuantum Machine LearningQuantum Deep Equilibrium Models
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circ…
Quantum Machine LearningHybrid model of the kernel method for quantum computers
The field of quantum machine learning is a promising way to lead to a revolution in intelligent data processing methods. In this way, a hybrid learning method based on classic kernel methods is proposed. This proposal al…
Quantum Machine LearningRobustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum rei…
Quantum Machine Learningreinforcement-learningReinforcement LearningMethod for noise-induced regularization in quantum neural networks
In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of …
Quantum Machine LearningAgnostic Process Tomography
Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of a…
Learning TheoryQuantum Machine LearningExploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks
With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which w…
Quantum Machine LearningQuantum-Trained Convolutional Neural Network for Deepfake Audio Detection
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neur…
DeepFake DetectionFace SwappingQuantum Machine LearningKACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection
Heart failure is a leading cause of global mortality, necessitating improved diagnostic strategies. Classical machine learning models struggle with challenges such as high-dimensional data, class imbalances, poor feature…
Conformal PredictionDiagnosticDisease PredictionKolmogorov-Arnold Networks+2Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
Image processing is one of the most promising applications for quantum machine learning (QML). Quanvolutional Neural Networks with non-trainable parameters are the preferred solution to run on current and near future qua…
QuantizationQuantum Machine LearningQ-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency o…
Hyperparameter OptimizationQuantum Machine LearningResource-efficient equivariant quantum convolutional neural networks
Equivariant quantum neural networks (QNNs) are promising quantum machine learning models that exploit symmetries to provide potential quantum advantages. Despite theoretical developments in equivariant QNNs, their implem…
Quantum Machine LearningMulti-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification
Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting opportunities to resolve, speed up, or refine the analysis of a wide range of computational problems. In the realm of biomedica…
Diagnosticfeature selectionPrognosisQuantum Machine LearningSatellite image classification with neural quantum kernels
Achieving practical applications of quantum machine learning for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a…
ClassificationEarth Observationimage-classificationImage Classification+2OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines
Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image gene…
Image GenerationQuantum Machine LearningQuForge: A Library for Qudits Simulation
Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing informa…
Quantum Machine LearningAQMLator -- An Auto Quantum Machine Learning E-Platform
A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be …
AutoMLQuantum Machine LearningA Hybrid Quantum Neural Network for Split Learning
Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-constrained clients to collaboratively tr…
Quantum Machine LearningQuantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks …
Drug DiscoveryMolecular Property PredictionProperty PredictionQuantum Machine Learning