Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Exploring Quantum Machine Learning for Weather Forecasting
Weather forecasting plays a crucial role in supporting strategic decisions across various sectors, including agriculture, renewable energy production, and disaster management. However, the inherently dynamic and chaotic …
Quantum Machine LearningWeather ForecastingQuantum Machine Learning for UAV Swarm Intrusion Detection
Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we…
Quantum Machine LearningIntrusion DetectionQuantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case
This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication systems, particularly focusing on 5G network…
Quantum Machine LearningSolving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the Optimal Power Flow
Conic programs arise broadly in physics, quantum information, machine learning, and engineering, many of which are defined over sparse graphs. Although such problems can be solved in polynomial time using classical inter…
Quantum Machine LearningQuantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder
Quantum machine learning deals with leveraging quantum theory with classic machine learning algorithms. Current research efforts study the advantages of using quantum mechanics or quantum information theory to accelerate…
Quantum Machine LearningQuantum Machine Learning for Optimizing Entanglement Distribution in Quantum Sensor Circuits
In the rapidly evolving field of quantum computing, optimizing quantum circuits for specific tasks is crucial for enhancing performance and efficiency. More recently, quantum sensing has become a distinct and rapidly gro…
Quantum Machine LearningReinforcement LearningReinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribu…
Quantum Machine LearningReinforcement LearningDifferentially Private Federated Quantum Learning via Quantum Noise
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attack…
Quantum Machine LearningFederated LearningAdversarial AttackQuantum Entanglement as Super-Confounding: From Bell's Theorem to Robust Machine Learning
Bell's theorem reveals a profound conflict between quantum mechanics and local realism, a conflict we reinterpret through the modern lens of causal inference. We propose and computationally validate a framework where qua…
Quantum Machine LearningCausal InferenceTowards Quantum Machine Learning for Malicious Code Analysis
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malwar…
Quantum Machine LearningMalware ClassificationBinary ClassificationMalware DetectionQuantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical approaches still face challenges in ach…
Quantum Machine LearningDrug DiscoveryIs data-efficient learning feasible with quantum models?
The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics rema…
Quantum Machine LearningRobust and Efficient Quantum Reservoir Computing with Discrete Time Crystal
The rapid development of machine learning and quantum computing has placed quantum machine learning at the forefront of research. However, existing quantum machine learning algorithms based on quantum variational algorit…
Quantum Machine LearningBinary ClassificationImage ClassificationQuantum Long Short-term Memory with Differentiable Architecture Search
Recent advances in quantum computing and machine learning have given rise to quantum machine learning (QML), with growing interest in learning from sequential data. Quantum recurrent models like QLSTM are promising for t…
Quantum Machine LearningReinforcement LearningMultimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations
Accurately predicting enzyme functionality remains one of the major challenges in computational biology, particularly for enzymes with limited structural annotations or sequence homology. We present a novel multimodal Qu…
Quantum Machine LearningAdversarial Robustness in Distributed Quantum Machine Learning
Studying adversarial robustness of quantum machine learning (QML) models is essential in order to understand their potential advantages over classical models and build trustworthy systems. Distributing QML models allows …
Quantum Machine LearningAdversarial RobustnessFederated LearningThe Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators
Quantum Reservoir Computing (QRC) uses quantum dynamics to efficiently process temporal data. In this work, we investigate a QRC framework based on two coupled Kerr nonlinear oscillators, a system well-suited for time-se…
Quantum Machine LearningCollaborative Filtering using Variational Quantum Hopfield Associative Memory
Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machin…
Quantum Machine LearningCollaborative FilteringRecommendation SystemsEnhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applications
Quantum machine learning (QML) presents potential for early industrial adoption, yet limited access to quantum hardware remains a significant bottleneck for deployment of QML solutions. This work explores the use of clas…
Quantum Machine LearningHybrid quantum tensor networks for aeroelastic applications
We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with variational quantum circuits, we demonst…
Time Series ClassificationQuantum Machine LearningDimensionality ReductionBinary Classification