Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Fixed-Reservoir vs Variational Quantum Architectures for Chaotic Dynamics: Benchmarking QRC and QPINN on the Lorenz System
Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systematically compare two quantum approaches for chaotic time-series predict…
Quantum Machine LearningOn the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these disti…
Quantum Machine LearningCancer ClassificationOption Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capi…
Quantum Machine LearningQuantum AI for Cancer Diagnostic Biomarker Discovery
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In thi…
Quantum Machine LearningCancer ClassificationHybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This pa…
Medical Image ClassificationQuantum Machine LearningCancer ClassificationFederated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater…
Quantum Machine LearningFederated LearningParameter-efficient Quantum Multi-task Learning
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined wit…
Quantum Machine LearningMulti-Task LearningSarcasm DetectionDesign Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-qu…
Quantum Machine LearningQuantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14\% leak prevalence, we tested ZZFeatureMap…
Quantum Machine LearningMagnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models operate in complex Hilbert spaces. This similarity suggests that using both the magnitude and phase of SAR dat…
Quantum Machine LearningNon-variational supervised quantum kernel methods: a review
Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning. Unlike variational quantum algorithms, which rely on gradient-based optimisation and may suffer from issues suc…
Quantum Machine LearningSoft-Quantum Algorithms
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parameters into gate-based operations and opti…
Quantum Machine LearningReinforcement LearningEliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
Quantum machine learning (QML) stands at the intersection of quantum computing and artificial intelligence, offering the potential to solve problems that remain intractable for classical methods. However, the current lan…
Quantum Machine LearningLieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks
Quantum Machine Learning (QML) is fundamentally limited by two challenges: barren plateaus (exponentially vanishing gradients) and the fragility of parameterized quantum circuits under noise. Despite extensive empirical …
Quantum Machine LearningQ-Bridge: Code Translation for Quantum Machine Learning via LLMs
Large language models have recently shown potential in bridging the gap between classical machine learning and quantum machine learning. However, the lack of standardized, high-quality datasets and robust translation fra…
Quantum Machine LearningCode TranslationCode GenerationHardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the …
Quantum Machine LearningComputational EfficiencyAnomaly DetectionSpectral methods: crucial for machine learning, natural for quantum computers?
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier s…
Quantum Machine LearningA PAC-Bayesian approach to generalization for quantum models
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacity rather than the specific function lear…
Quantum Machine LearningModel selection in hybrid quantum neural networks with applications to quantum transformer architectures
Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices of encodings, quantum circuit designs and initialization strategies to…
Quantum Machine LearningThe Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers
Variational Quantum Algorithms (VQAs) have been extensively researched for applications in Quantum Machine Learning (QML), Optimization, and Molecular simulations. Although designed for Noisy Intermediate-Scale Quantum (…
Quantum Machine Learning