Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Lean classical-quantum hybrid neural network model for image classification
The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classif…
ClassificationDecision Makingimage-classificationImage Classification+2Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement
In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise con…
Quantum Machine LearningThe role of data-induced randomness in quantum machine learning classification tasks
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of da…
Binary ClassificationClassificationQuantum Machine LearningPredicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region
In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum n…
Quantum Machine LearningMixed-State Quantum Denoising Diffusion Probabilistic Model
Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilisti…
DenoisingmodelQuantum Machine LearningIntegrating Machine Learning and Quantum Circuits for Proton Affinity Predictions
A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of p…
Quantum Machine LearningA quantum inspired predictor of Parkinsons disease built on a diverse, multimodal dataset
Parkinsons disease, the fastest growing neurodegenerative disorder globally, has seen a 50 percent increase in cases within just two years. As speech, memory, and motor symptoms worsen over time, early diagnosis is cruci…
Quantum Machine LearningAdversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent p…
Data PoisoningQuantum Machine LearningQuantum Kernel-Based Long Short-term Memory
The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel…
Edge-computingQuantum Machine LearningDigital-Analog Quantum Machine Learning
Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previ…
Quantum Machine LearningQuantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of …
Quantum Machine LearningLeveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quan…
Quantum Machine LearningComputable Model-Independent Bounds for Adversarial Quantum Machine Learning
By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well-documented to be vulnerable to malicious …
Quantum Machine LearningUnderstanding Generalization in Quantum Machine Learning with Margins
Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those r…
Quantum Machine LearningEmpirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data
The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational pow…
ClassificationQuantum Machine LearningQuantum Diffusion Models for Few-Shot Learning
Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets. Most state-of-the-art QML algorithms…
DenoisingFew-Shot LearningQuantum Machine LearningExpressivity of deterministic quantum computation with one qubit
Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its subuniversality with limited quantum resou…
Quantum Machine LearningDistribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages
The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by…
Quantum Machine LearningInformation plane and compression-gnostic feedback in quantum machine learning
The information plane (Tishby et al. arXiv:physics/0004057, Shwartz-Ziv et al. arXiv:1703.00810) has been proposed as an analytical tool for studying the learning dynamics of neural networks. It provides quantitative ins…
Information PlaneQuantum Machine LearningAn Exponential Separation Between Quantum and Quantum-Inspired Classical Algorithms for Linear Systems
Achieving a provable exponential quantum speedup for an important machine learning task has been a central research goal since the seminal HHL quantum algorithm for solving linear systems and the subsequent quantum recom…
Quantum Machine LearningRecommendation Systems