Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Scalable quantum dynamics compilation via quantum machine learning
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared …
Out-of-Distribution GeneralizationQuantum Machine LearningLatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder
Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating cl…
Anomaly DetectionImage GenerationManagementQuantum Machine LearningQuantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Althoug…
Quantum Machine LearningMachine-learning based high-bandwidth magnetic sensing
Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this co…
Quantum Machine LearningSensitivityEarly Detection of Coronary Heart Disease Using Hybrid Quantum Machine Learning Approach
Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and …
GPUQuantum Machine LearningRaspberry Pi 5SpecificityQuantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact
Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data. While classical machine learning (CML) appr…
BenchmarkingQuantum Machine LearningCompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging d…
DiagnosticQuantum Machine LearningExploring Biological Neuronal Correlations with Quantum Generative Models
Understanding of how biological neural networks process information is one of the biggest open scientific questions of our time. Advances in machine learning and artificial neural networks have enabled the modeling of ne…
Quantum Machine LearningLearning Robust Observable to Address Noise in Quantum Machine Learning
Quantum Machine Learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challenges in QML is dealing with noise in quan…
Quantum Machine LearningGeneralization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely unexplored factor: the generalization err…
ArticlesQuantum Machine LearningQuantum Kernel Methods under Scrutiny: A Benchmarking Study
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel methods (QKMs) have gained increasing attention with regard to both probing promising applications and delivering intriguing resea…
BenchmarkingQuantum Machine LearningTraining quantum machine learning models on cloud without uploading the data
Based on the linearity of quantum unitary operations, we propose a method that runs the parameterized quantum circuits before encoding the input data. This enables a dataset owner to train machine learning models on quan…
Quantum Machine LearningCan Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum machine learning (GQML) approach with emb…
Quantum Machine LearningOptimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
In the current Noisy Intermediate Scale Quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms. Quantum Reservoir Computing (QRC) is a type of Quantum Machine Learning algo…
DenoisingQuantum Machine LearningTime SeriesTime Series Forecasting+1Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents
Deep Learning algorithms, such as those used in Reinforcement Learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for some c…
Quantum Machine Learningreinforcement-learningReinforcement LearningQuantum Machine Learning for Anomaly Detection in Consumer Electronics
Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing w…
Anomaly DetectionQuantum Machine LearningAI-driven Reverse Engineering of QML Models
Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the research of QML models, there is a rise in th…
Quantum Machine LearningVerifiable cloud-based variational quantum algorithms
Variational quantum algorithms (VQAs) have shown potential for quantum advantage with noisy intermediate-scale quantum (NISQ) devices for quantum machine learning (QML). However, given the high cost and limited availabil…
Quantum Machine LearningQuantum Convolutional Neural Networks are (Effectively) Classically Simulable
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, …
Quantum Machine LearningThe curse of random quantum data
Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike its classical counterparts, the role of …
Quantum Machine Learning