Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
We introduce the Partitioned Hybrid Quantum Fourier Neural Operator (PHQFNO), a generalization of the Quantum Fourier Neural Operator (QFNO) for scientific machine learning. PHQFNO partitions the Fourier operator computa…
Quantum Machine LearningQuantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
Quantum federated learning (QFL) has been recently introduced to enable a distributed privacy-preserving quantum machine learning (QML) model training across quantum processors (clients). Despite recent research efforts,…
Federated LearningPrivacy PreservingQuantum Machine LearningEmotion Recognition in Older Adults with Quantum Machine Learning and Wearable Sensors
We investigate the feasibility of inferring emotional states exclusively from physiological signals, thereby presenting a privacy-preserving alternative to conventional facial recognition techniques. We conduct a perform…
Quantum Machine LearningEmotion RecognitionParametrized Quantum Circuit Learning for Quantum Chemical Applications
In the field of quantum machine learning (QML), parametrized quantum circuits (PQCs) -- constructed using a combination of fixed and tunable quantum gates -- provide a promising hybrid framework for tackling complex mach…
Quantum Machine LearningQuantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks
Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work, we proposed novel Trojan attacks based …
Quantum Machine LearningQMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
Quantum machine learning (QML) has emerged as a promising direction in the noisy intermediate-scale quantum (NISQ) era, offering computational and memory advantages by harnessing superposition and entanglement. However, …
Quantum Machine LearningA Resource Efficient Quantum Kernel
Quantum processors may enhance machine learning by mapping high-dimensional data onto quantum systems for processing. Conventional feature maps, for encoding data onto a quantum circuit are currently impractical, as the …
Quantum Machine LearningComputational EfficiencySelective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
Quantum Machine Learning (QML) has seen significant advancements, driven by recent improvements in Noisy Intermediate-Scale Quantum (NISQ) devices. Leveraging quantum principles such as entanglement and superposition, qu…
Quantum Machine LearningBinary ClassificationImage ClassificationQuantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling
Quantum computing has opened new opportunities to tackle complex machine learning tasks, for instance, high-dimensional data representations commonly required in intelligent transportation systems. We explore quantum mac…
Quantum Machine LearningResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary diffe…
Quantum Machine LearningIterative Quantum Feature Maps
Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have demonstrated rigorous end-to-end quantu…
Contrastive Learningimage-classificationImage ClassificationQuantum Machine LearningA Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as quantum autoencoders can be constrained by…
Dimensionality ReductionQuantum Machine LearningOn Quantum BSDE Solver for High-Dimensional Parabolic PDEs
We propose a quantum machine learning framework for approximating solutions to high-dimensional parabolic partial differential equations (PDEs) that can be reformulated as backward stochastic differential equations (BSDE…
Model-based Reinforcement LearningQuantum Machine LearningComponent Based Quantum Machine Learning Explainability
Explainable ML algorithms are designed to provide transparency and insight into their decision-making process. Explaining how ML models come to their prediction is critical in fields such as healthcare and finance, as it…
Quantum Machine LearningVQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning, yet their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges…
Quantum Machine LearningThe interplay of robustness and generalization in quantum machine learning
While adversarial robustness and generalization have individually received substantial attention in the recent literature on quantum machine learning, their interplay is much less explored. In this chapter, we address th…
Adversarial RobustnessQuantum Machine LearningTime SeriesTime Series AnalysisSuperposed Parameterised Quantum Circuits
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across outputs. These constraints limit expres…
Quantum Machine LearningQuantum Graph Transformer for NLP Sentiment Classification
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critical. We present the Quantum Graph Transfor…
ClassificationLanguage ModelingLanguage ModellingQuantum Machine Learning+2Devanagari Digit Recognition using Quantum Machine Learning
Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and l…
Handwritten Digit RecognitionQuantum Machine LearningTQml Simulator: Optimized Simulation of Quantum Machine Learning
Hardware-efficient circuits employed in Quantum Machine Learning are typically composed of alternating layers of uniformly applied gates. High-speed numerical simulators for such circuits are crucial for advancing resear…
Quantum Machine Learning