Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Shor…
BenchmarkingHyperparameter OptimizationProduction ForecastingQuantum Machine Learning+3Blind quantum machine learning with quantum bipartite correlator
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintainin…
Privacy PreservingQuantum Machine LearningA Hyperparameter Study for Quantum Kernel Methods
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up the possibility of prescreening datasets…
Hyperparameter OptimizationQuantum Machine LearningMachine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classifi…
Hyperparameter OptimizationQuantum Machine LearningCertainty In, Certainty Out: REVQCs for Quantum Machine Learning
The field of Quantum Machine Learning (QML) has emerged recently in the hopes of finding new machine learning protocols or exponential speedups for classical ones. Apart from problems with vanishing gradients and efficie…
Quantum Machine LearningA Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. …
Quantum Machine LearningSurveyQuantum Machine Learning in Climate Change and Sustainability: a Review
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as…
Quantum Machine LearningNon-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learning tasks remains a crucial question. Th…
Quantum Machine LearningFederated Quantum Machine Learning with Differential Privacy
The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently…
Binary ClassificationFederated LearningPrivacy PreservingQuantum Machine LearningGenerative quantum machine learning via denoising diffusion probabilistic models
Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability…
DenoisingQuantum Machine LearningText GenerationBringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the A…
AutoMLBenchmarkingQuantum Machine LearningLearning unitaries with quantum statistical queries
We propose several algorithms for learning unitary operators from quantum statistical queries (QSQs) with respect to their Choi-Jamiolkowski state. Quantum statistical queries capture the capabilities of a learner with l…
Quantum Machine LearningLearning Quantum Processes with Quantum Statistical Queries
In this work, we initiate the study of learning quantum processes from quantum statistical queries. We focus on two fundamental learning tasks in this new access model: shadow tomography of quantum processes and process …
BenchmarkingCryptanalysisQuantum Machine LearningApproximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises. However, their performance highly…
image-classificationImage ClassificationInductive BiasQuantum Machine LearningTensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimization techniques used for training the cont…
Binary ClassificationQuantum Machine LearningTensor NetworksA hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud
As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The qua…
Cloud ComputingGenerative Adversarial NetworkQuantum Machine LearningSLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity de…
Quantum Machine LearningStatistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantum state that encodes useful data. As a q…
Quantum Machine LearningExpressive variational quantum circuits provide inherent privacy in federated learning
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standard neural network-based federated learnin…
Federated LearningQuantum Machine LearningPotential and limitations of random Fourier features for dequantizing quantum machine learning
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQ…
Quantum Machine Learningregression