Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and identification tasks. A subclass of QML met…
Dimensionality ReductionGenerative Adversarial NetworkImage GenerationImage Manipulation+2The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the q…
Quantum Machine LearningImproving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a w…
Quantum Machine LearningHarmonic (Quantum) Neural Networks
Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions from industria…
Inductive BiasQuantum Machine LearningRobot NavigationQuantum Phase Recognition using Quantum Tensor Networks
Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanc…
image-classificationImage ClassificationQuantum Machine LearningTensor NetworksEnhancing Quantum Adversarial Robustness by Randomized Encodings
The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving ce…
Adversarial RobustnessQuantum Machine LearningAn exponentially-growing family of universal quantum circuits
Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or barren plateaus, renders the training impossi…
Quantum Machine LearningNumerical evidence against advantage with quantum fidelity kernels on classical data
Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to l…
Inductive BiasQuantum Machine LearningSoftware Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (…
Multi-agent Reinforcement LearningQuantum Machine Learningreinforcement-learningReinforcement Learning+1Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled…
Adversarial AttackAdversarial Attack DetectionAutonomous VehiclesBenchmarking+1An Invitation to Distributed Quantum Neural Networks
Deep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise to the concept of distributed deep learni…
Quantum Machine LearningQuantum Split Neural Network Learning using Cross-Channel Pooling
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, q…
Federated LearningPrivacy PreservingQuantum Machine LearningQuantum State TomographyHybrid quantum neural network for drug response prediction
Cancer is one of the leading causes of death worldwide. It is caused by a variety of genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each pa…
Drug Response PredictionPredictionQuantum Machine LearningResource frugal optimizer for quantum machine learning
Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical probl…
Quantum Machine LearningVariational Quantum Kernels with Task-Specific Quantum Metric Learning
Quantum kernel methods, i.e., kernel methods with quantum kernels, offer distinct advantages as a hybrid quantum-classical approach to quantum machine learning (QML), including applicability to Noisy Intermediate-Scale Q…
feature selectionMetric LearningQuantum Machine LearningTransfer LearningReservoir Computing via Quantum Recurrent Neural Networks
Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Ex…
Quantum Machine LearningTime Series AnalysisTime Series PredictionQuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. This work bridges the g…
Operator learningQuantum Machine LearningeXplainable AI for Quantum Machine Learning
Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measur…
Explainable Artificial Intelligence (XAI)Quantum Machine LearningUnclonability and Quantum Cryptanalysis: From Foundations to Applications
The impossibility of creating perfect identical copies of unknown quantum systems is a fundamental concept in quantum theory and one of the main non-classical properties of quantum information. This limitation imposed by…
CryptanalysisQuantum Machine LearningProjection Valued Measure-based Quantum Machine Learning for Multi-Class Classification
In recent years, quantum machine learning (QML) has been actively used for various tasks, e.g., classification, reinforcement learning, and adversarial learning. However, these QML studies are unable to carry out complex…
Multi-class ClassificationQuantum Machine Learning