Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Security Concerns in Quantum Machine Learning as a Service
Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML models can effectively generalize from l…
Quantum Machine LearningBenchmarking quantum machine learning kernel training for classification tasks
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods …
BenchmarkingQuantum Machine LearningDistributed and Secure Kernel-Based Quantum Machine Learning
Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides enhanced security through fundamental pri…
Quantum Machine LearningQuantum Gradient Class Activation Map for Model Interpretability
Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not been thoroughly investigated. This work pro…
modelQuantum Machine LearningQuantum Machine Learning: Performance and Security Implications in Real-World Applications
Quantum computing has garnered significant attention in recent years from both academia and industry due to its potential to achieve a "quantum advantage" over classical computers. The advent of quantum computing introdu…
CPUQuantum Machine LearningCertifiably Robust Encoding Schemes
Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these models are susceptible to attacks that ma…
Quantum Machine LearningDiscrete Randomized Smoothing Meets Quantum Computing
Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial at…
Quantum Machine LearningQuantum Long Short-Term Memory for Drug Discovery
Quantum computing combined with machine learning (ML) is an extremely promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effecti…
Drug DiscoveryQuantum Machine LearningQuantum Machine Learning Architecture Search via Deep Reinforcement Learning
The rapid advancement of quantum computing (QC) and machine learning (ML) has given rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the strengths of quantum computing to propel ML …
Deep Reinforcement LearningQuantum Machine Learningreinforcement-learningReinforcement LearningReverse Map Projections as Equivariant Quantum Embeddings
We introduce the novel class $(E_\alpha)_{\alpha \in [-\infty,1)}$ of reverse map projection embeddings, each one defining a unique new method of encoding classical data into quantum states. Inspired by well-known map pr…
Quantum Machine LearningQEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet, have achieved considerable success in …
EEGQuantum Machine LearningHybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models
Numerous established machine learning models and various neural network architectures can be restructured as Quadratic Unconstrained Binary Optimization (QUBO) problems. A significant challenge in Adiabatic Quantum Machi…
Fraud DetectionQuantum Machine LearningSchedulingQuadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis
As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomiz…
Quantum Machine LearningTime SeriesTime Series AnalysisTime Series ClassificationInvestigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Survey
In recent years, variational quantum circuits (VQCs) have been widely explored to advance quantum circuits against classic models on various domains, such as quantum chemistry and quantum machine learning. Similar to cla…
Quantum Machine LearningSurveySystematic Literature ReviewQuantum Supervised Learning
Recent advancements in quantum computing have positioned it as a prospective solution for tackling intricate computational challenges, with supervised learning emerging as a promising domain for its application. Despite …
Quantum Machine LearningQuantum Computing for Climate Resilience and Sustainability Challenges
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promi…
Computational EfficiencyManagementQuantum Machine LearningA Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classic…
DiversityQuantum Machine LearningIn Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum nature presents inherent challenges. One maj…
Quantum Machine LearningQuantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms rel…
Quantum Machine LearningQuantum Natural Stochastic Pairwise Coordinate Descent
Variational quantum algorithms, optimized using gradient-based methods, often exhibit sub-optimal convergence performance due to their dependence on Euclidean geometry. Quantum natural gradient descent (QNGD) is a more e…
Computational EfficiencyQuantum Machine Learning