Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder-decoder a…
Quantum Machine LearningAnomaly DetectionQuantum Generative Diffusion Model for Real-World Time Series
Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency. Quantum machine learning offe…
Quantum Machine LearningTailor Made Embeddings for Quantum Machine Learning
Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations. In this work, we extend this paradig…
Quantum Machine LearningSelf-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmers, or Self-Modulating QFWP, which extend…
Quantum Machine LearningQuantum Convolutional Neural Networks for Groundwater Heat Plume Prediction: A Surrogate Modeling Approach
Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In this work, we explore a Quantum Convolutional Neural Network (QCNN) as …
Quantum Machine LearningNo Reference-Free Generalization in Quantum Machine Learning
Quantum machine learning is often motivated by the exponentially large state space of quantum systems, but this promise leaves a basic generalization problem unresolved: how can a learner assign different meanings to uns…
Quantum Machine LearningTrainable Photonic Measurement for Physics-Informed PDE Learning
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored. Physics-…
Quantum Machine LearningEnhancing Quantum Machine Learning with Anyons
The power of quantum computing and quantum machine learning relies on harnessing uniquely quantum phenomena as computational resources. While superposition, coherence and entanglement have been central to this effort, th…
Quantum Machine LearningQuantum Machine Learning for Industrial Applications
Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consump…
Quantum Machine LearningAn LLM System for Autonomous Variational Quantum Circuit Design
The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit des…
Quantum Machine LearningImage ClassificationCode GenerationQuantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learn…
Quantum Machine LearningJGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied…
Quantum Machine LearningTrainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule. Recent work has provided a method to classically train QCBMs with Instantaneous Quantum Polynomia…
Quantum Machine LearningGenerative Quantum Data Embeddings for Supervised Learning
Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are embedded into quantum states. Yet the use of a fixed embedding circuit…
Quantum Machine LearningDo We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing…
Quantum Machine LearningQuantum-Enhanced Adversarial Robustness in Artificial Intelligence
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Ad…
Quantum Machine LearningAdversarial RobustnessLatent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such en…
Quantum Machine LearningQML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
Quantum machine learning (QML) is moving from research prototypes to deployed cloud services. As QML enters regulated industries, the integrity of the quantum stage becomes a practical concern on two fronts: noisy hardwa…
Quantum Machine LearningA Matched Spectral Benchmark of Quantum Inspired Feature Maps
Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access with classical models. We isolate one central component of this claim:…
Quantum Machine LearningQ-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints. Existing approaches …
Neural Architecture SearchQuantum Machine LearningImage Classification