Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application
The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with…
ClassificationQuantum Machine LearningAnalyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment
We explored the possible benefits of integrating quantum simulators in a "hybrid" quantum machine learning (QML) workflow that uses both classical and quantum computations in a high-performance computing (HPC) environmen…
CPUGPUQuantum Machine LearningThe Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models
Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendo…
Quantum Machine LearningHybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches
Quantum machine learning (QML) investigates how quantum phenomena can be exploited in order to learn data in an alternative way, \textit{e.g.} by means of a quantum computer. While recent results evidence that QML models…
image-classificationImage ClassificationQuantum Machine LearningSketch RecognitionQuantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification
Machine learning and quantum computing are being progressively explored to shed light on possible computational approaches to deal with hitherto unsolvable problems. Classical methods for machine learning are ubiquitous …
Quantum Machine LearningExploiting the equivalence between quantum neural networks and perceptrons
Quantum machine learning models based on parametrized quantum circuits, also called quantum neural networks (QNNs), are considered to be among the most promising candidates for applications on near-term quantum devices. …
Inductive BiasQuantum Machine LearningQuantum Curriculum Learning
Quantum machine learning (QML) requires significant quantum resources to address practical real-world problems. When the underlying quantum information exhibits hierarchical structures in the data, limitations persist in…
Quantum Machine LearningFlood Prediction Using Classical and Quantum Machine Learning Models
This study investigates the potential of quantum machine learning to improve flood forecasting we focus on daily flood events along Germany's Wupper River in 2023 our approach combines classical machine learning techniqu…
ManagementQuantum Machine LearningHarnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States
In the present study, we use cross-domain classification using quantum machine learning for quantum advantages to readdress the entanglement versus separability paradigm. The inherent structure of quantum states and its …
Classificationdomain classificationQuantum Machine LearningQuantum Machine Learning: The Superhero That Classical Machine Learning Never Knew It Needed
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in c…
Quantum Machine LearningA Matrix Product State Model for Simultaneous Classification and Generation
Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematical frameworks in this domain is tensor ne…
Quantum Machine LearningTensor NetworksMachine Learning Applications of Quantum Computing: A Review
At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bo…
ArticlesQuantum Machine LearningOn the relation between trainability and dequantization of variational quantum learning models
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical machine learning. Successful QML mod…
Quantum Machine LearningRelationReinforcement learning-based architecture search for quantum machine learning
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting mod…
Model-based Reinforcement LearningQuantum Machine Learningreinforcement-learningReinforcement LearningStatistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learni…
Quantum Machine LearningTraining-efficient density quantum machine learning
Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model family that prepares mix…
LEMMAMixture-of-ExpertsQuantum Machine LearningQuantum Visual Feature Encoding Revisited
Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantu…
Quantum Machine LearningGraph Neural Networks on Quantum Computers
Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recom…
Graph AttentionGraph Neural NetworkQuantum Machine LearningRecommendation SystemsMulti-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification
In recent years, with the development of quantum machine learning, quantum neural networks (QNNs) have gained increasing attention in the field of natural language processing (NLP) and have achieved a series of promising…
Quantum Machine LearningSentenceSentence EmbeddingSentence-Embedding+2An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum…
Quantum Machine Learning