Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications, pulse-based models must be both expres…
Quantum Machine LearningEnhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
This paper develops a hybrid quantum approach for graph-based semi-supervised learning to enhance performance in scenarios where labeled data is scarce. We introduce two enhanced quantum models, the Improved Laplacian Qu…
Quantum Machine LearningSuperior resilience to poisoning and amenability to unlearning in quantum machine learning
The reliability of artificial intelligence hinges on the integrity of its training data, a foundation often compromised by noise and corruption. Here, through a comparative study of classical and quantum neural networks …
Quantum Machine LearningEvaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploiting the quantum computing paradigm. One of…
Quantum Machine LearningTensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing
Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we introduce TensorHyper-VQC, a novel tensor-tra…
Quantum Machine LearningStructured quantum learning via em algorithm for Boltzmann machines
Quantum Boltzmann machines (QBMs) are generative models with potential advantages in quantum machine learning, yet their training is fundamentally limited by the barren plateau problem, where gradients vanish exponential…
Quantum Machine LearningEmbedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means disti…
Quantum Machine LearningPennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on remote APIs, introducing challenges relate…
Quantum Machine LearningReinforcement LearningCode GenerationFD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report
The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and q…
Quantum Machine LearningBinary ClassificationFeature EngineeringFraud DetectionQuantum Machine Learning Playground
This article introduces an innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms. Our work is inspired by the success of classical machine learning visualization tools,…
Quantum Machine LearningBenchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we benchmark six models, XGBoost, Random Fores…
Quantum Machine LearningMeta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
The preparation of quantum Gibbs states is a fundamental challenge in quantum computing, essential for applications ranging from modeling open quantum systems to quantum machine learning. Building on the Meta-Variational…
Quantum Machine LearningOld Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning
One of the key obstacles in traditional deep learning is the reduction in model transparency caused by increasingly intricate model functions, which can lead to problems such as overfitting and excessive confidence in pr…
Quantum Machine LearningQuantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for…
Quantum Machine LearningQuantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical data processing. However, due to the expo…
Quantum Machine LearningStochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI
Efficient entanglement strategies are essential for advancing variational quantum circuits (VQCs) for quantum machine learning (QML). However, most current approaches use fixed entanglement topologies that are not adapti…
Quantum Machine LearningFormal Verification of Variational Quantum Circuits
Variational quantum circuits (VQCs) are a central component of many quantum machine learning algorithms, offering a hybrid quantum-classical framework that, under certain aspects, can be considered similar to classical d…
Quantum Machine LearningQuantum Transfer Learning to Boost Dementia Detection
Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes…
Quantum Machine LearningBinary ClassificationImage ClassificationTransfer LearningEntangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
Quantum Machine Learning (QML) systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing b…
Model extractionQuantum Machine LearningAdvances in Machine Learning: Where Can Quantum Techniques Help?
Quantum Machine Learning (QML) represents a promising frontier at the intersection of quantum computing and artificial intelligence, aiming to leverage quantum computational advantages to enhance data-driven tasks. This …
Quantum Machine Learning