The field of quantum computing is rapidly advancing, with significant developments in machine learning and optimization. Researchers are exploring the potential of quantum computing to improve the efficiency and accuracy of various machine learning models and algorithms. One notable trend is the integration of quantum computing with classical machine learning techniques, such as federated learning and neural networks. This has led to the development of new frameworks and models that leverage quantum computing to enhance the security, privacy, and performance of machine learning systems. Additionally, quantum optimization techniques are being applied to solve complex problems in fields such as wireless communication, reservoir seepage, and power grid control. Noteworthy papers include Quantum Vanguard, which proposes a server-optimized privacy-fortified federated intelligence framework for future vehicles, and Quantum Topological Graph Neural Networks, which introduces a novel framework for detecting complex fraud patterns in large-scale financial networks. Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations also demonstrates the potential of quantum-classical hybrid models for solving partial differential equations in reservoir engineering.
Quantum Advances in Machine Learning and Optimization
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Quantum-Inspired Spectral Geometry for Neural Operator Equivalence and Structured Pruning
Quantum Vanguard: Server Optimized Privacy Fortified Federated Intelligence for Future Vehicles
AtomGraph: Tackling Atomicity Violation in Smart Contracts using Multimodal GCNs
Quantum Optimization in Wireless Communication Systems: Principles and Applications
A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design
A2G-QFL: Adaptive Aggregation with Two Gains in Quantum Federated learning
Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload
Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning
Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement
Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection