Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults
Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work in…
Anomaly DetectionDiagnosticQuantum Machine LearningImproving Quantum Machine Learning via Heat-Bath Algorithmic Cooling
This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling proces…
Quantum Machine LearningComparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
As financial fraud becomes increasingly complex, effective detection methods are essential. Quantum Machine Learning (QML) introduces certain capabilities that may enhance both accuracy and efficiency in this area. This …
Fraud DetectionQuantum Machine LearningQuantum memristors for neuromorphic quantum machine learning
Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quant…
Quantum Machine LearningTowards structure-preserving quantum encodings
Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings…
Metric LearningQuantum Machine LearningTopological Data AnalysisA Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
Quantum machine learning has gained attention for its potential to address computational challenges. However, whether those algorithms can effectively solve practical problems and outperform their classical counterparts,…
Anomaly DetectionQuantum Machine LearningOpportunities and limitations of explaining quantum machine learning
A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active fie…
Quantum Machine LearningQuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, …
Quantum Machine LearningTime SeriesTime Series ForecastingWeather ForecastingQuantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities
Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly. Existing methods commonly extract log se…
Anomaly DetectionModel SelectionQuantum Machine LearningSpecificityEvolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity
Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machine learning (QML) algorithms to address a…
Quantum Machine LearningThe Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits
Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term quantum devices and the early fault-tol…
Quantum Machine LearningData-Dependent Generalization Bounds for Parameterized Quantum Models Under Noise
Quantum machine learning offers a transformative approach to solving complex problems, but the inherent noise hinders its practical implementation in near-term quantum devices. This obstacle makes it difficult to underst…
Generalization BoundsLearning TheoryQuantum Machine LearningOptimizing Hyperparameters for Quantum Data Re-Uploaders in Calorimetric Particle Identification
We present an application of a single-qubit Data Re-Uploading (QRU) quantum model for particle classification in calorimetric experiments. Optimized for Noisy Intermediate-Scale Quantum (NISQ) devices, this model require…
global-optimizationQuantum Machine LearningRegression and Classification with Single-Qubit Quantum Neural Networks
Since classical machine learning has become a powerful tool for developing data-driven algorithms, quantum machine learning is expected to similarly impact the development of quantum algorithms. The literature reflects a…
Quantum Machine LearningregressionExperimental Machine Learning with Classical and Quantum Data via NMR Quantum Kernels
Kernel methods map data into high-dimensional spaces, enabling linear algorithms to learn nonlinear functions without explicitly storing the feature vectors. Quantum kernel methods promise efficient learning by encoding …
Quantum Machine LearningData Efficient Prediction of excited-state properties using Quantum Neural Networks
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their …
Gaussian ProcessesQuantum Machine LearningQuantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities
Software defect prediction is a critical aspect of software quality assurance, as it enables early identification and mitigation of defects, thereby reducing the cost and impact of software failures. Over the past few ye…
Quantum Machine LearningMitigating exponential concentration in covariant quantum kernels for subspace and real-world data
Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map. In fact, there exist classification pr…
Quantum Machine LearningSchedulingLEP-QNN: Loan Eligibility Prediction Using Quantum Neural Networks
Predicting loan eligibility with high accuracy remains a significant challenge in the finance sector. Accurate predictions enable financial institutions to make informed decisions, mitigate risks, and effectively adapt s…
PredictionQuantum Machine LearningReinforcement learning to learn quantum states for Heisenberg scaling accuracy
Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that uti…
Meta-LearningQuantum Machine LearningReinforcement Learning (RL)