Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantum machine learning framework for longitudinal biomedical studies
Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative di…
Quantum Machine LearningIntroduction to Quantum Machine Learning and Quantum Architecture Search
Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging int…
Quantum Machine LearningAdaptive Non-local Observable on Quantum Neural Networks
Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heisenberg picture, we propose an adaptive n…
Quantum Machine LearningQuorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently emerged as a powerful tool for tackling sev…
Anomaly DetectionQuantum Machine LearningUnsupervised Anomaly DetectionPredictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks
Accurate prediction and stabilization of blast furnace temperatures are crucial for optimizing the efficiency and productivity of steel production. Traditional methods often struggle with the complex and non-linear natur…
PredictionQuantum Machine LearningFine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
Large language models (LLM) have achieved remarkable outcomes in addressing complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet few works have explored the potential of LLM in…
MathQuantum Machine LearningAn Efficient Quantum Classifier Based on Hamiltonian Representations
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. However, many studies rely on toy datasets or heavy feature reduction, raising concerns abou…
image-classificationImage ClassificationQuantum Machine LearningDeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search
Demonstrating quantum advantage using conventional quantum algorithms remains challenging on current noisy gate-based quantum computers. Automated quantum circuit synthesis via quantum machine learning has emerged as a p…
Program SynthesisQuantum Machine LearningSymbolic RegressionAutomating quantum feature map design via large language models
Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, des…
Quantum Machine LearningQuantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis
Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning. It promises to unlock unparalleled capabilities in data analysis, model building, and problem-solvin…
Quantum Machine LearningA Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Euclidean assumptions. In parallel, Quantum Machine Learning (QML) has eme…
Quantum Machine LearningStructural Health MonitoringDetecting underdetermination in parameterized quantum circuits
A central question in machine learning is how reliable the predictions of a trained model are. Reliability includes the identification of instances for which a model is likely not to be trusted based on an analysis of th…
Quantum Machine LearningQuantum Deep Sets and Sequences
This paper introduces the quantum deep sets model, expanding the quantum machine learning tool-box by enabling the possibility of learning variadic functions using quantum systems. A couple of variants are presented for …
Density EstimationQuantum Machine LearningK-P Quantum Neural Networks
We present an extension of K-P time-optimal quantum control solutions using global Cartan $KAK$ decompositions for geodesic-based solutions. Extending recent time-optimal \emph{constant-$\theta$} control results, we inte…
Quantum Machine LearningHQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure
Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifier (HQCC) is proposed. It opens a practi…
Quantum Machine LearningQuantum-Assisted Machine Learning Models for Enhanced Weather Prediction
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gat…
Binary ClassificationClassificationPredictionQuantum Machine Learning+1Quantum Doeblin Coefficients: Interpretations and Applications
In classical information theory, the Doeblin coefficient of a classical channel provides an efficiently computable upper bound on the total-variation contraction coefficient of the channel, leading to what is known as a …
FairnessQuantum Machine LearningMolecular Quantum Transformer
The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges such as high computational cost and memory usa…
Quantum Machine LearningMultiple Embeddings for Quantum Machine Learning
This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel qua…
DiversityQuantum Machine LearningQuantum Complex-Valued Self-Attention Model
Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, …
modelMulti-class ClassificationQuantum Machine Learning