Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
Kernel methods are typically formulated under the assumption of exact, noise-free access to the Gram matrix. However, in emerging settings each kernel entry must be inferred from noisy observations, and its accuracy depe…
Quantum Machine LearningQuantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive …
Quantum Machine LearningAnomaly DetectionHybrid Quantum-Classical Neural Architecture Search
Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with pa…
Neural Architecture SearchQuantum Machine LearningQuantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation
With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and mode…
Quantum Machine LearningReinforcement LearningSemantic CommunicationSAFE Quantum Machine Learning with Variational Quantum Classifiers
We propose a variational quantum classifier operating on high dimensional deep representations via amplitude encoding, stabilized by a learnable classical pre encoding layer.By combining normalized amplitude embeddings w…
Quantum Machine LearningWinning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Inste…
Quantum Machine LearningQuantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine learning is a field that studies these vulne…
Quantum Machine LearningQuantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features
Quantum machine learning has emerged as a promising tool for pattern recognition, yet many audio-focused approaches still treat spectrograms as generic images and do not explicitly exploit their time-frequency structure.…
Quantum Machine LearningAudio Deepfake DetectionHybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
Classical generative adversarial networks (GANs) have been applied to generate adversarial network traffic capable of attacking intrusion detection systems, but they suffer from shortcomings such as the need for large am…
Quantum Machine LearningIntrusion DetectionStochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation
Quantum machine learning increasingly relies on pure-state representations, motivating generative models that sample directly in quantum representation space rather than perturbing classical inputs and re-encoding. We in…
Quantum Machine LearningEntanglement is Half the Story: Post-Selection vs. Partial Traces
While tensor networks have their traditional application in simulating quantum systems, in the recent decade they have gathered interest as machine learning models. We combine the experience from both fields and derive h…
Quantum Machine LearningFrom Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data
High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum …
Quantum Machine LearningConformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadratic inference complexity and unreliable uncertainty quantification …
Quantum Machine LearningQuantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) is a popular certified training method in…
Quantum Machine LearningAdversarial RobustnessControlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations remains a major challenge for practical d…
Quantum Machine LearningDefending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cause the model to make…
Quantum Machine LearningImage ClassificationProvable and scalable quantum Gaussian processes for quantum learning
Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning frameworks that are simple, interpretable, scala…
Quantum Machine LearningGaussian ProcessesEfficient Mutation Testing of Quantum Machine Learning Models
Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing compl…
Quantum Machine LearningHQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation
Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine …
Quantum Machine LearningSemantic SegmentationImage SegmentationDo Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tab…
Quantum Machine Learning