108 papers indexed here
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Quantum-driven constraint on complex systems aims to accelerate solution processes by dynamically adjusting constraint parameters. This paper explores the potential of quantum annealing and variational quantum eigensolver (VQE) to optimize these parameters. The core claim is to implement an algorithm that dynamically a...
Quantum Reinforcement Learning (QRL) represents a novel approach to reinforcement learning that leverages the unique capabilities of quantum computing to enhance learning efficiency and enable the exploration of complex behavioral rules. This paper explores the theoretical foundations, design principles, and potential...
Quantum state simulation is a cornerstone of quantum computing, aiming to accurately represent and manipulate the behavior of quantum systems. However, traditional methods of quantum state simulation, such as the Density Matrix Renormalization Group (DMRG) method and the Variational Quantum Eigensolver (VQE), often suf...
The increasing sophistication of cyber threats demands more effective and reliable detection systems. However, many current Artificial Intelligence (AI)-based cybersecurity solutions operate as "black boxes," lacking transparency and hindering trust. This research addresses this critical gap by integrating Explainable...
The increasing deployment of complex AI models, particularly deep neural networks, raises significant concerns about their interpretability and trustworthiness. Existing XAI techniques often provide post-hoc explanations that lack a clear connection to the model's underlying reasoning. This paper proposes a novel appro...
This paper investigates the application of Explainable Artificial Intelligence (XAI) techniques to the detection of software vulnerabilities. Traditional vulnerability detection methods often rely on black-box machine learning models, lacking transparency and hindering understanding of the underlying reasons for vulner...
The increasing deployment of complex AI models, particularly deep neural networks, raises significant concerns about their interpretability and trustworthiness. Existing XAI techniques often provide post-hoc explanations that lack a clear connection to the model's underlying reasoning. This paper proposes a novel appro...
This paper introduces a novel framework for knowledge representation and reasoning that synergistically combines the strengths of neural networks and symbolic logic. The core claim is to construct a system capable of both learning intricate patterns from data using neural networks and performing precise reasoning and i...
This paper explores a novel approach to reasoning about temporal relationships by integrating temporal logic with neural networks. The core idea is to train a neural network to understand and apply the rules of temporal logic, thereby enabling it to reason more robustly and reliably about events and their temporal depe...
This paper presents a novel approach to distributed learning leveraging Federated Bayesian Networks (FBNs). Traditional federated learning methods struggle with capturing complex, non-independent data distributions, often leading to suboptimal global models. We introduce a framework where multiple devices collaborative...
This paper presents a novel approach to distributed machine learning, termed Federated Learning with Differential Privacy (FLDP), which addresses the critical challenge of privacy preservation in collaborative model training. Traditional federated learning techniques, while mitigating the risks associated with centrali...
Federated learning (FL) presents a promising approach to training machine learning models on decentralized data sources while preserving user privacy. However, traditional FL methods often rely on a central server, introducing a single point of failure and potential privacy risks. This paper proposes a novel decentrali...
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