Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Coreset selection can accelerate quantum machine learning models with provable generalization
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical mac…
Quantum Machine LearningDifferentiable Quantum Architecture Search for Quantum Reinforcement Learning
Differentiable quantum architecture search (DQAS) is a gradient-based framework to design quantum circuits automatically in the NISQ era. It was motivated by such as low fidelity of quantum hardware, low flexibility of c…
Q-LearningQuantum Machine Learningreinforcement-learningReinforcement LearningQuantum Machine Learning in the Cognitive Domain: Alzheimer's Disease Study
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, primarily affecting the elderly population and leading to significant cognitive decline. This decline manifests in various mental faculties such …
Quantum Machine LearningAll you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
Variational algorithms require architectures that naturally constrain the optimisation space to run efficiently. In geometric quantum machine learning, one achieves this by encoding group structure into parameterised qua…
AllInductive BiasQuantum Machine LearningA quantum tug of war between randomness and symmetries on homogeneous spaces
We explore the interplay between symmetry and randomness in quantum information. Adopting a geometric approach, we consider states as $H$-equivalent if related by a symmetry transformation characterized by the group $H$.…
Quantum Machine LearningDetecting Violations of Differential Privacy for Quantum Algorithms
Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and othe…
Product RecommendationQuantum Machine LearningTensor NetworksAdversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection
We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first applic…
image-classificationImage ClassificationQuantum Machine LearningTransfer LearningLet Quantum Neural Networks Choose Their Own Frequencies
Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's …
Quantum Machine LearningA hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery
The field of drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins, especially when such proteins directly influence disease progression. However, est…
Drug DiscoveryPredictionQuantum Machine LearningMutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance
One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine le…
Generative Adversarial NetworkImage GenerationQuantum Machine LearningWhat can we learn from quantum convolutional neural networks?
Quantum machine learning (QML) shows promise for analyzing quantum data. A notable example is the use of quantum convolutional neural networks (QCNNs), implemented as specific types of quantum circuits, to recognize phas…
Feature EngineeringQuantum Machine LearningSub-universal variational circuits for combinatorial optimization problems
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research. This work introduces a novel class of clas…
Combinatorial OptimizationQuantum Machine LearningApplication of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples
Over the past few years, there has been significant interest in Quantum Machine Learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the proper…
Classificationimage-classificationImage ClassificationMulti-class Classification+1QKSAN: A Quantum Kernel Self-Attention Network
Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models. Nevertheless, many Quantum Machine Learning (QML) models lack the abi…
Binary ClassificationComputational EfficiencyQuantum Machine LearningMosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers
Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with man…
Image GenerationQuantum Machine LearningQuantum-Inspired Machine Learning: a Survey
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However…
Quantum Machine LearningSurveySeveral fitness functions and entanglement gates in quantum kernel generation
Quantum machine learning (QML) represents a promising frontier in the quantum technologies. In this pursuit of quantum advantage, the quantum kernel method for support vector machine has emerged as a powerful approach. E…
Quantum Machine LearningEvaluating quantum generative models via imbalanced data classification benchmarks
A limited set of tools exist for assessing whether the behavior of quantum machine learning models diverges from conventional models, outside of abstract or theoretical settings. We present a systematic application of ex…
Explainable artificial intelligenceQuantum Machine LearningQuantum State Tomography using Quantum Machine Learning
Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements…
Quantum Machine LearningQuantum State TomographyImplementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance
Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being pro…
Drug DiscoveryGenerative Adversarial NetworkQuantum Machine Learning