Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Neural Networks for Programming Quantum Annealers
Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind quantum machine learning are similar to…
Quantum Machine LearningFinancial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performanc…
Fraud DetectionQuantum Machine LearningApplication-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selectio…
BenchmarkingGPUQuantum Machine LearningHigh-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning
We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the…
Quantum Machine LearningSemisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
Anomaly detection (AD) involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden pattern…
Anomaly DetectionQuantum Machine LearningregressionHybrid quantum transfer learning for crack image classification on NISQ hardware
Quantum computers possess the potential to process data using a remarkably reduced number of qubits compared to conventional bits, as per theoretical foundations. However, recent experiments have indicated that the pract…
Edge Detectionimage-classificationImage ClassificationQuantum Machine Learning+1A supervised hybrid quantum machine learning solution to the emergency escape routing problem
Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machine learning to optimize emergency evacua…
Quantum Machine LearningQuantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of near error-free quantum computers in the not-so-distant future, it is i…
Quantum Machine LearningHigher-order topological kernels via quantum computation
Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data. TDA enhances the analysis of objects by embedding them into a simplicial complex and extracting useful …
Quantum Machine LearningTopological Data AnalysisQuantum Autoencoders for Learning Quantum Channel Codes
This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible chan…
Quantum Machine LearningExplainable quantum regression algorithm with encoded data structure
Hybrid variational quantum algorithms (VQAs) are promising for solving practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy …
Combinatorial Optimizationfeature selectionQuantum Machine LearningregressionQuantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications
The past decade has witnessed significant advancements in quantum hardware, encompassing improvements in speed, qubit quantity, and quantum volume-a metric defining the maximum size of a quantum circuit effectively imple…
Quantum Machine LearningSurveyInter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?
Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in mach…
Predictive Process MonitoringQuantum Machine LearningExponential separations between classical and quantum learners
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with classical data. In this paper we addres…
Learning TheoryQuantum Machine LearningUnderstanding quantum machine learning also requires rethinking generalization
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to underst…
MemorizationQuantum Machine LearningUniversal adversarial perturbations for multiple classification tasks with quantum classifiers
Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversari…
Continual LearningQuantum Machine LearningEnhancing variational quantum state diagonalization using reinforcement learning techniques
The variational quantum algorithms are crucial for the application of NISQ computers. Such algorithms require short quantum circuits, which are more amenable to implementation on near-term hardware, and many such methods…
Quantum Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SQL2Circuits: Estimating Metrics for SQL Queries with a Quantum Natural Language Processing Method
In recent years, advances in quantum computing have led to accelerating research on quantum applications across fields. Here, we introduce a quantum machine learning model as a potential solution to the classical questio…
Binary ClassificationQuantum Machine LearningSplitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large n…
Quantum Machine LearningWeight Re-Mapping for Variational Quantum Algorithms
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promis…
Quantum Machine Learning