Quantum entanglement is a fundamental resource for distributed quantum computing, enabling complex computations that are intractable for classical computers. However, establishing and maintaining entanglement across a network of nodes presents significant challenges due to decoherence and communication overhead. This paper explores the concept of dynamic entanglement distribution, a novel framework utilizing reinforcement learning to intelligently allocate entanglement among nodes in a distributed computing system, optimizing performance based on workload and resource constraints. We introduce a reinforcement learning agent trained to dynamically adjust entanglement distribution to minimize errors and maximize computational efficiency. The proposed approach addresses key limitations of existing entanglement distribution techniques, offering a more adaptive and intelligent solution for harnessing the power of quantum entanglement in distributed computing. This research aims to develop a practical implementation of a dynamic entanglement distribution strategy, paving the way for more efficient and robust quantum computations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to scheduling optimization that synergistically combines the strengths of dynamic programming (DP) and reinforcement learning (RL). Traditional DP methods struggle with complex scheduling scenarios due to their exponential computational complexity, particularly when dealing with intricate constraints and a large state space. This work addresses this limitation by employing a hybrid framework where DP generates an initial, feasible schedule and defines a cost function, while an RL agent dynamically refines this schedule based on real-time system state. The RL agent learns a policy to adapt scheduling parameters, effectively pruning the search space and accelerating convergence towards optimal solutions. The core innovation lies in the iterative interaction between DP and RL, leading to a more robust and efficient scheduling process. This approach demonstrates improved scalability and performance compared to pure DP solutions, particularly in scenarios with dynamic and complex constraints. We introduce a framework that balances the deterministic nature of DP with the adaptive capabilities of RL, offering a promising solution for a wide range of scheduling challenges.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of adaptive quantum state collapse within the context of machine learning training. Traditional training methods often rely on static parameters, limiting the model's ability to adapt to complex data patterns. We propose a novel approach leveraging reinforcement learning to dynamically adjust the quantum state collapse process, optimizing model performance. The core mechanism involves a reinforcement learning agent that iteratively refines the collapse based on the model's output and data distribution, leading to improved generalization and convergence speed. This work presents a framework for dynamically shaping the quantum state to enhance machine learning capabilities. The potential for significant gains in model accuracy and training efficiency is a key focus.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Emergent Multi-Agent Temporal Dynamics (EMATD) framework, a novel approach to modeling temporal dynamics within complex systems. EMATD leverages reinforcement learning to train agents within a dynamically changing environment, resulting in the emergence of collective temporal patterns. The core claim is that this system offers a fundamentally new perspective on understanding how complex behaviors arise from the interactions of individual agents, moving beyond simple individual response to a holistic, emergent system dynamics. This work explores the design and implementation of the EMATD framework, detailing the core mechanisms and demonstrating its effectiveness through a simulated environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Deep generative models have achieved remarkable success in various domains, including image generation, text generation, and music generation. However, these models often operate as black boxes, lacking transparency and interpretability. Furthermore, they are highly vulnerable to adversarial attacks, where subtle perturbations in the input can lead to drastically different and incorrect outputs. This research addresses these critical limitations by proposing a novel framework that integrates explainable AI (XAI) techniques with adversarial training methods. We leverage SHAP (SHapley Additive exPlanations) values to provide insights into the generative model's decision-making process, enabling us to understand the factors driving the generated outputs. Simultaneously, we employ adversarial training to enhance the model's robustness against adversarial attacks. Our approach aims to create a deep generative model that is both interpretable and resilient to malicious inputs. The key contributions of this work are the combined approach of XAI-driven interpretation and adversarial defense, offering a pathway towards more trustworthy and secure deep generative models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the development of a novel approach to computational creativity centered around generative process modeling. Current AI systems striving for creative output frequently fall short of mimicking the nuanced and often unpredictable nature of human creativity. A key limitation is the absence of a mechanism for representing and controlling the creative process itself. This research proposes a framework that explicitly models the creative process as a series of discrete computational steps, encompassing stages such as ideation, experimentation, and refinement. The core mechanism involves constructing a generative process model (GPM) that can actively guide and shape the creative journey. This approach moves beyond simply generating outputs and instead focuses on the strategic manipulation of the underlying creative process, offering a more robust and controllable pathway to creative outcomes. The paper details the design principles of the GPM and outlines a potential architecture for its implementation, highlighting its potential to overcome current limitations in AI-driven creative systems. The presented model offers a framework for understanding and replicating the iterative and often messy nature of human creativity, ultimately leading to more sophisticated and genuinely creative AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates a novel approach to enhancing the adversarial robustness of neural networks by utilizing generative models. The core idea revolves around training a generative model to accurately reconstruct the input data, thereby creating a robust representation. This reconstructed data is then used as input to the neural network, significantly reducing the impact of adversarial perturbations. We demonstrate that this defense mechanism, leveraging generative models, provides a more effective strategy compared to traditional methods. Our approach addresses a critical vulnerability in current neural network designs and offers a promising direction for building more resilient and reliable AI systems. We present a theoretical framework and outline the key components of this defense strategy. The central contribution lies in the systematic application of generative models to proactively defend against adversarial attacks, rather than reacting to them post-hoc. The results presented indicate a substantial improvement in the network's resilience to adversarial examples.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The design of constraint systems is a critical component of many engineering and scientific applications. Traditional methods often involve extensive manual design, which can be time-consuming and prone to error. This paper introduces a novel system, Dynamically Generated Modular Constraint Systems (DGMCS), that leverages generative AI to automatically create and adapt constraint sets based on the properties of a mathematical model. The system aims to improve performance by streamlining the constraint design process, reducing manual effort, and enabling the creation of more robust and adaptable constraint sets. The core mechanism centers on training a generative AI model on a comprehensive dataset of constraint systems and iteratively refining it through a process of adaptation and refinement. This approach eliminates the need for manual design and allows for the creation of highly customized constraint sets. This work demonstrates the potential of AI to significantly enhance the efficiency and quality of constraint system design.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the development of a novel approach to computational creativity centered around generative process modeling. Current AI systems striving for creative output frequently fall short of mimicking the nuanced and often unpredictable nature of human creativity. A key limitation is the absence of a mechanism for representing and controlling the creative process itself. This research proposes a framework that explicitly models the creative process as a series of discrete computational steps, encompassing stages such as ideation, experimentation, and refinement. The core mechanism involves constructing a generative process model (GPM) that can actively guide and shape the creative journey. This approach moves beyond simply generating outputs and instead focuses on the strategic manipulation of the underlying creative process, offering a more robust and controllable pathway to creative outcomes. The paper details the design principles of the GPM and outlines a potential architecture for its implementation, highlighting its potential to overcome current limitations in AI-driven creative systems. The presented model offers a framework for understanding and replicating the iterative and often messy nature of human creativity, ultimately leading to more sophisticated and genuinely creative AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the possibility of emergent linguistic grammar arising from the dynamic interactions within a large, interconnected network of agents. The central premise is that complex linguistic structures, traditionally considered the product of human cognitive development, can spontaneously emerge through self-organization and evolutionary processes. We propose a computational model simulating a network where agents communicate using a simplified set of rules, allowing for the observation of pattern formation that resembles grammatical structures. The model leverages network dynamics, specifically focusing on synchronization and feedback loops, to drive the emergence of linguistic features. The results demonstrate that, under specific conditions, complex patterns consistent with syntactic rules – such as hierarchical structures and dependency relationships – can arise without explicit programming of these rules. This research offers a new perspective on the origins of language, shifting the investigation from solely human cognition to the dynamics of distributed systems, and suggesting that emergent properties can arise in complex networks with minimal centralized control. The key metric for assessing emergent grammar is the degree of regularity and predictability in the patterns observed within the network. Further research will explore the influence of network topology, agent interaction rules, and the diversity of communication strategies on the resulting linguistic structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Concurrent programming presents significant challenges in software development due to the inherent complexities of managing multiple processes interacting simultaneously. Traditional methods of verification, such as testing and debugging, often prove inadequate for uncovering subtle concurrency-related errors. This paper proposes a novel approach to formally specify and verify concurrent systems using abstract state machines (ASMs) augmented with a priority-based scheduling mechanism. The core idea is to represent the system's behavior as a state machine, capturing the system's states and transitions, and then introduce priority-based scheduling to model the order in which these transitions occur. This approach allows for rigorous analysis and verification of the system's correctness, providing a formal foundation for ensuring reliable concurrent software. We present a formal specification language based on ASMs, detailing the key components and methodology. The system's behavior is expressed as a set of states and transitions, where the priority-based scheduling algorithm dictates the execution order. The paper outlines the core concepts and demonstrates the potential of this methodology for improving the reliability and safety of concurrent systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to program error diagnosis based on the principles of chaos theory. Traditional methods for detecting and diagnosing errors in software often rely on static analysis, dynamic testing, or formal verification, which can be insufficient for handling the inherent complexity and emergent behavior of modern software systems. This research posits that program execution can be modeled as a chaotic system, exhibiting extreme sensitivity to initial conditions and generating complex, unpredictable sequences of states. By analyzing these chaotic properties, we can develop a more accurate and robust diagnostic system. The core idea is to identify deviations from expected chaotic behavior as indicators of potential errors. We present a framework for quantifying chaos in program execution, utilizing metrics such as Lyapunov exponents and fractal dimensions. The system's sensitivity to minor variations in input data or internal states can then be leveraged to pinpoint the source of errors. This approach offers the potential for early error detection, reduced debugging time, and improved software reliability. The presented methodology provides a fundamentally different perspective on program error diagnosis, shifting from deterministic analysis to a dynamic, chaotic perspective.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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