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 adjusts constraint parameters on complex systems (e.g., protein folding, DNA sequencing) to achieve optimal solution speed and accuracy. The approach leverages quantum annealing for its ability to escape local optima and VQE for efficient exploration of the solution space. This research investigates the feasibility and potential advantages of employing quantum computing for optimization tasks within complex systems, offering a novel methodology for enhancing computational efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 applications of a QRL algorithm, focusing on its ability to learn intricate patterns and make superior decisions within challenging environments. The core mechanism centers around quantum superposition and entanglement, strategically employed to represent the state space and guide the agent's exploration, resulting in improved learning speed and robustness. We present a detailed framework for implementing this algorithm, discussing its potential impact on diverse reinforcement learning scenarios, particularly those encountered in edge applications. The paper concludes with a discussion of ongoing research directions and future prospects for QRL.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 suffer from significant computational challenges, particularly as the size of the quantum system increases. This paper proposes a novel approach to quantum state simulation leveraging the power of Graph Neural Networks (GNNs). We represent a quantum state as a graph, where nodes correspond to individual quantum states and edges represent the correlations between them. A GNN is then trained to learn the complex relationships within the quantum state, enabling efficient and scalable simulation. Our method significantly reduces the computational burden associated with traditional techniques, offering a promising path towards practical quantum state simulation. The core claim is the utilization of graph neural networks' structural information for efficient quantum state simulation. The core mechanism involves representing quantum states as nodes in a graph and employing GNNs to learn relationships between them, facilitating efficient quantum state simulation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 AI (XAI) techniques with causal reasoning to create a more understandable and actionable cybersecurity threat detection system. We construct a causal Bayesian network that explicitly models the relationships between network events and potential threats. Subsequently, XAI methods are applied to interpret the reasoning process of the system, highlighting the key causal pathways leading to threat detections. This approach offers a novel way to explain the complex decision-making processes of AI systems in cybersecurity, fostering greater trust, facilitating targeted investigations, and ultimately improving threat response capabilities. The core contribution lies in the synergistic combination of causal inference and XAI, moving beyond simply explaining correlations to providing genuine causal understanding.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 approach to building truly explainable AI systems by leveraging probabilistic programming languages and causal Bayesian networks. The core idea is to explicitly represent the model's decision-making process through a causal Bayesian network, which allows for the tracing of influence and identification of key features. We demonstrate how probabilistic programming facilitates the construction of these networks, providing a framework for generating transparent and understandable AI systems. The proposed method shifts the focus from black-box model explanations to a white-box understanding of the model's causal structure, thereby addressing a critical limitation in current XAI methodologies. This approach offers a robust foundation for building AI systems that not only achieve high accuracy but also provide clear and justifiable explanations for their decisions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 vulnerability identification. This research proposes a novel approach leveraging XAI to provide interpretable insights into the causes of vulnerabilities, ultimately leading to more effective remediation strategies. The system utilizes explainable machine learning models to analyze program code, identify potential vulnerabilities, and generate targeted repair suggestions. The core claim is that XAI significantly improves vulnerability detection by providing actionable explanations alongside vulnerability predictions. Quantitative analysis demonstrates the increased accuracy and efficiency of the XAI-driven approach compared to conventional methods. Key words: Explainable AI, Vulnerability Detection, Software Security, Machine Learning, Code Analysis, Interpretability.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 approach to building truly explainable AI systems by leveraging probabilistic programming languages and causal Bayesian networks. The core idea is to explicitly represent the model's decision-making process through a causal Bayesian network, which allows for the tracing of influence and identification of key features. We demonstrate how probabilistic programming facilitates the construction of these networks, providing a framework for generating transparent and understandable AI systems. The proposed method shifts the focus from black-box model explanations to a white-box understanding of the model's causal structure, thereby addressing a critical limitation in current XAI methodologies. This approach offers a robust foundation for building AI systems that not only achieve high accuracy but also provide clear and justifiable explanations for their decisions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 inference via symbolic logic. The proposed mechanism leverages neural networks to capture the underlying relationships and structures within knowledge, while symbolic logic provides a robust and verifiable framework for deduction and decision-making. This approach addresses a critical limitation of existing AI systems – the inherent trade-off between the precision of symbolic methods and the generalization capabilities of neural networks. The resulting neuro-symbolic system offers a pathway to more reliable, explainable, and adaptable AI. We outline the key components of the framework, discuss potential architectures, and explore initial considerations for implementation. This work represents a significant step towards creating intelligent systems that can truly understand and reason about the world. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 dependencies. We propose a mechanism where the neural network learns both the syntax and semantics of temporal logic expressions. This allows the system to effectively represent and manipulate temporal constraints, ultimately leading to improved reasoning performance compared to purely neural or purely symbolic approaches. The work highlights the potential for creating hybrid systems that leverage the strengths of both formal logic and deep learning, offering a path toward more explainable and trustworthy AI systems for temporal reasoning tasks. The key contribution lies in the formalization of temporal reasoning within a neural network framework, providing a structured method for encoding temporal relationships.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 collaboratively learn a Bayesian Network model through decentralized updates, explicitly addressing these dependency challenges. The core idea is to allow devices to share probabilistic models and inference results, creating a system that adapts to local data characteristics while maintaining data privacy. The framework incorporates techniques for managing network topology, handling heterogeneous data distributions, and ensuring convergence of the collaborative learning process. We demonstrate the potential of this approach through a theoretical analysis and outline key considerations for practical implementation, highlighting its advantages over conventional federated learning systems. The primary contribution lies in the systematic application of Bayesian Networks to federated learning, providing a robust mechanism for learning from correlated data sources.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 centralized data collection, still expose model updates, potentially revealing sensitive information about the underlying data. FLDP overcomes this limitation by directly applying differential privacy to the model aggregation process. Specifically, we introduce a locally sensitive differential privacy mechanism that injects noise into each node's model update before aggregation. This ensures that the impact of any single node's contribution on the global model is bounded, effectively protecting individual data privacy. The proposed method allows for reliable model convergence while rigorously upholding privacy guarantees. We demonstrate the feasibility and effectiveness of FLDP through a theoretical analysis and outline a practical implementation strategy. The key contribution of this work lies in the shift from applying differential privacy solely to data training to directly incorporating it into the model aggregation phase, creating a more robust and privacy-preserving distributed learning system.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 decentralized federated learning system that leverages threshold cryptography and differential privacy to address these concerns. The core claim is that protecting user privacy in federated learning remains a significant challenge, and this system provides a robust solution. The system operates through a series of distributed rounds where participants collaboratively update model parameters using threshold cryptography to ensure secure aggregation and differential privacy to mitigate individual data exposure. This approach eliminates the need for a central server, enhancing both privacy and security. The system is designed for scalability and adaptability, making it suitable for various decentralized data scenarios. This work contributes to the development of more secure and privacy-preserving federated learning solutions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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