Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
Quantum machine learning (QML) models conventionally rely on repeated measurements (shots) of observables to obtain reliable predictions. This dependence on large shot budgets leads to high inference cost and time overhe…
Quantum Machine LearningRe-uploading quantum data: a universal function approximator for quantum inputs
Quantum data re-uploading has proved powerful for classical inputs, where repeatedly encoding features into a small circuit yields universal function approximation. Extending this idea to quantum inputs remains underexpl…
Quantum Machine Learningorb-QFL: Orbital Quantum Federated Learning
Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination…
Quantum Machine LearningFederated LearningKnowledge Distillation for Variational Quantum Convolutional Neural Networks on Heterogeneous Data
Distributed quantum machine learning faces significant challenges due to heterogeneous client data and variations in local model structures, which hinder global model aggregation. To address these challenges, we propose …
Quantum Machine LearningKnowledge DistillationImpact of Single Rotations and Entanglement Topologies in Quantum Neural Networks
In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks t…
Quantum Machine LearningImage ClassificationQuantum Enhanced Anomaly Detection for ADS-B Data using Hybrid Deep Learning
The emerging field of Quantum Machine Learning (QML) has shown promising advantages in accelerating processing speed and effectively handling the high dimensionality associated with complex datasets. Quantum Computing (Q…
Quantum Machine LearningAnomaly DetectionQuantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
In this work, we introduce the Quantum Generative Adversarial Autoencoder (QGAA), a quantum model for generation of quantum data. The QGAA consists of two components: (a) Quantum Autoencoder (QAE) to compress quantum sta…
Quantum Machine LearningQuantum Variational Activation Functions Empower Kolmogorov-Arnold Networks
Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by…
Computational EfficiencyQuantum Machine LearningImage ClassificationHybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critica…
Quantum Machine LearningDimensionality ReductionFeature EngineeringQuantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers
This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading st…
Quantum Machine LearningHybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction
Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly acce…
Quantum Machine LearningDrug DiscoveryInvestigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quantum physics, it can surpass the limitati…
Quantum Machine LearningQuantum Architecture Search for Solving Quantum Machine Learning Tasks
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variat…
Quantum Machine LearningReinforcement LearningPrediction of Stocks Index Price using Quantum GANs
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for stock price prediction. Financial markets are inherently complex, marked by high volatility and intricate patterns that tradi…
Quantum Machine LearningStock Price PredictionQCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Cardinality estimation is an important part of query optimization in DBMS. We develop a Quantum Cardinality Estimation (QCardEst) approach using Quantum Machine Learning with a Hybrid Quantum-Classical Network. We define…
Quantum Machine LearningFrom Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of a direct quantum mechanical realization…
Quantum Machine LearningReinforcement LearningA Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets
The development of noise-resilient quantum machine learning (QML) algorithms is critical in the noisy intermediate-scale quantum (NISQ) era. In this work, we propose a quantum bagging framework that uses QMeans clusterin…
Quantum Machine LearningQuantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
Optimizing high-degree of freedom robotic manipulators requires searching complex, high-dimensional configuration spaces, a task that is computationally challenging for classical methods. This paper introduces a quantum …
Quantum Machine LearningQ-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics
The rise of machine learning in safety-critical systems has paralleled advancements in quantum computing, leading to the emerging field of Quantum Machine Learning (QML). While safety monitoring has progressed in classic…
Quantum Machine LearningExoplanetary atmospheres retrieval via a quantum extreme learning machine
The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality…
Quantum Machine Learning