Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel…
Quantum Machine LearningHybrid quantum cycle generative adversarial network for small molecule generation
The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integrati…
DiversityDrug DesignDrug DiscoveryGenerative Adversarial Network+1Photovoltaic power forecasting using quantum machine learning
Predicting solar panel power output is crucial for advancing the transition to renewable energy but is complicated by the variable and non-linear nature of solar energy. This is influenced by numerous meteorological fact…
Quantum Machine LearningTime SeriesTime Series PredictionOn fundamental aspects of quantum extreme learning machines
Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate - the quantum reservo…
Quantum Machine LearningDistributed Quantum Neural Networks via Partitioned Features Encoding
Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qub…
Quantum Machine LearningFederated Quantum Long Short-term Memory (FedQLSTM)
Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different ta…
Federated LearningQuantum Machine LearningThe role of data embedding in equivariant quantum convolutional neural networks
Geometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainability and generalization. Recently this ide…
ClassificationQuantum Machine LearningTowards an in-depth detection of malware using distributed QCNN
Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of explor…
Malware DetectionQuantum Machine LearningHarnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (…
Binary ClassificationQuantum Machine LearningChallenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning…
Quantum Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms
In this pioneering research paper, we present a groundbreaking exploration into the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs). Our objectiv…
Quantum Machine LearningSelf-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
Chebyshev polynomials have shown significant promise as an efficient tool for both classical and quantum neural networks to solve linear and nonlinear differential equations. In this work, we adapt and generalize this fr…
Quantum Machine LearningA Novel Image Classification Framework Based on Variational Quantum Algorithms
Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the…
Classificationimage-classificationImage ClassificationQuantum Machine LearningRadio Signal Classification by Adversarially Robust Quantum Machine Learning
Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Research…
Classificationimage-classificationImage ClassificationQuantum Machine LearningImprovement in Variational Quantum Algorithms by Measurement Simplification
Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future. In this work, we review simple rules in basic quantum circui…
Quantum Machine LearningImproving Parameter Training for VQEs by Sequential Hamiltonian Assembly
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs). Similar to deep learning, vanishing gradients pose immense problems in the trainability of PQCs, which …
Quantum Machine LearningPrivacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limi…
Distributed ComputingFederated LearningIncremental LearningPrivacy Preserving+1Advantage of Quantum Machine Learning from General Computational Advantages
An overarching milestone of quantum machine learning (QML) is to demonstrate the advantage of QML over all possible classical learning methods in accelerating a common type of learning task as represented by supervised l…
Quantum Machine LearningQuantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings
Deep metric learning has recently shown extremely promising results in the classical data domain, creating well-separated feature spaces. This idea was also adapted to quantum computers via Quantum Metric Learning(QMeL).…
Metric LearningQuantum Machine LearningTripletHybrid Quantum Neural Network in High-dimensional Data Classification
The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle. We introduce a novel model architecture t…
ClassificationDeep LearningQuantum Machine Learning