This paper introduces a novel approach to computation security leveraging the principles of spectral theory. Traditional computational security relies heavily on cryptographic methods, which are increasingly vulnerable to advancements in computing power and algorithmic attacks. We propose a framework that abstracts computation into a spectral graph, allowing us to analyze and defend against malicious activities—specifically, data manipulation (tampering), eavesdropping, and deception—using spectral analysis techniques. The core idea is to represent the computational process as a graph where nodes represent operations and edges represent data flows. Analyzing the spectrum of this graph provides a robust method for identifying anomalies and detecting attacks. This work demonstrates a fundamentally new approach to security, shifting the focus from purely cryptographic solutions to a more holistic, data-driven perspective informed by spectral analysis. The proposed method offers a potentially more resilient defense against evolving threats in the computational domain.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the protection of quantum entanglement states through topological principles. The core claim is that embedding entangled states within systems exhibiting non-trivial topological properties can lead to enhanced long-term fidelity. The fundamental mechanism involves leveraging the topological robustness of these structures to suppress the detrimental effects of local perturbations on the delicate entangled correlations. We explore various topological systems, focusing on their ability to shield entangled states from decoherence. The theoretical framework presented here offers a novel approach to building robust quantum information processing architectures, particularly for quantum computing applications. We demonstrate, through mathematical analysis, that the topological protection significantly improves the preservation of entanglement against environmental noise. Specifically, we consider a model system—a chain of interacting spin-1/2 particles—and derive the equations governing the evolution of the entangled state under the influence of both Hamiltonian terms and external perturbations. Our results highlight the potential of topological protection for achieving high-fidelity quantum entanglement over extended periods. The key equation governing the evolution of the entangled state is given by: ψ(t) = U(t)ψ(0) where ψ(t) is the state vector at time t, ψ(0) is the initial state vector, and U(t) is the time-evolution operator. We also explore the concept of topological invariants, such as the braid group, to characterize the topological properties of the system. The braiding of these topological elements can be used to manipulate the entangled state without destroying the entanglement. The preservation of entanglement is quantified by the fidelity, defined as: F(t) = |⟨ψ(t)|ψ(0)⟩|2 where ⟨ψ(t)|ψ(0)⟩ represents the overlap between the final and initial states. Our analysis demonstrates that the fidelity decays much slower in topological systems compared to non-topological systems. Furthermore, we investigate different types of topological protection, including chiral edge states and non-abelian braiding. We present a general framework for assessing the topological protection of any quantum system, emphasizing the importance of understanding the interplay between the topological structure and the quantum state. Finally, we discuss the challenges and future directions in realizing topological quantum computation. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to computation security leveraging the principles of spectral theory. Traditional computational security relies heavily on cryptographic methods, which are increasingly vulnerable to advancements in computing power and algorithmic attacks. We propose a framework that abstracts computation into a spectral graph, allowing us to analyze and defend against malicious activities—specifically, data manipulation (tampering), eavesdropping, and deception—using spectral analysis techniques. The core idea is to represent the computational process as a graph where nodes represent operations and edges represent data flows. Analyzing the spectrum of this graph provides a robust method for identifying anomalies and detecting attacks. This work demonstrates a fundamentally new approach to security, shifting the focus from purely cryptographic solutions to a more holistic, data-driven perspective informed by spectral analysis. The proposed method offers a potentially more resilient defense against evolving threats in the computational domain.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to computing, termed Dynamic Semantic Network Neuro-Morphic Computing, which leverages the principles of biological neural networks to achieve parallel, adaptive learning, and reasoning for complex data structures. The core idea is to mimic the dynamic connectivity and synaptic plasticity mechanisms found in biological neurons, creating a programmable neuro-morphic architecture. This architecture utilizes dynamically adjustable neural network connections and synaptic strengths based on input data and learning algorithms. A temporal signal processing and feedback mechanism simulates the dynamic behavior of biological neural networks, combined with Graph Neural Networks (GNNs) to construct and update dynamic semantic networks for representing and inferring relationships within data. This approach addresses the limitations of existing neuro-morphic computing, which primarily focuses on static hardware architectures, and traditional GNNs facing inefficiencies in handling large-scale, dynamic semantic networks. The resulting system aims to provide real-time learning and reasoning capabilities for complex data relationships, with potential benefits of low power consumption and high parallelism. The system is formally defined as follows: Let *S* = {*s*1, *s*2, ..., *s*K} be a set of nodes representing data elements. Let *E* = {*e*1, *e*2, ..., *e*N} be a set of edges representing relationships between nodes. Let *W* = {*w*ij} be a matrix representing the connection weights between nodes *s*i and *s*j, where *w*ij ∈ ℝ. Let *θ* = {*θ*ij} be a matrix representing the synaptic strengths between nodes *s*i and *s*j, where *θ*ij ∈ ℝ. Let *a*i ∈ ℝd be the activation value of node *s*i at time *t*. Let *l*i ∈ ℝ be the learning rate for node *s*i at time *t*. Let *h*i ∈ ℝd be the hidden state of node *s*i at time *t*. The dynamic update rule for node activation is given by: *a*i(t+1) = σ(*∑*j (*w*ij*h*j(t+1)) + *θ*ij *a*i(t+1)) where σ is an activation function (e.g., sigmoid, ReLU). The dynamic update rule for hidden state is given by: *h*i(t+1) = *f*(*a*i(t+1)) where *f* is a function that transforms the activation value into a hidden state. The learning rule updates the connection weights and synaptic strengths as follows: *w*ij(t+1) = *w*ij(t) + *l*i *∑*k (*w*ik(t+1) (*a*k(t+1)) ) *θ*ij(t+1) = *θ*ij(t) + *l*i *∑*k (*w*ik(t+1) (*a*k(t+1)) ) where *l*i is the learning rate. The system's performance is evaluated based on metrics such as accuracy, convergence time, and energy consumption. The core architecture will be implemented using a neuromorphic hardware platform, potentially utilizing spiking neural networks (SNNs) for efficient temporal processing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel algorithm for dynamic topology optimization, leveraging the unique properties of quantum computing to dynamically adjust network parameters. The core mechanism utilizes quantum entanglement and superposition to efficiently compute and optimize the topology of a network, leading to improved performance compared to traditional optimization techniques. We propose a quantum-enhanced approach to network design, focusing on the dynamic adjustment of node and edge weights to achieve optimal network behavior. This work explores the potential of quantum computation to address challenges in network routing, congestion control, and resource allocation, offering a promising avenue for enhancing network efficiency and resilience. The proposed algorithm demonstrates improved results through extensive simulations and theoretical analysis, highlighting the potential for significant advancements in network management.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to computing leveraging dynamic topology neural-spike networks. The core idea centers on mimicking the self-adaptive topology structures found in biological neural networks to achieve higher efficiency and robustness in complex computational tasks. We introduce a programmable hardware platform composed of simulated neurons with dynamic connections and synaptic plasticity. Utilizing machine learning algorithms, specifically reinforcement learning, we continuously optimize the network topology in response to task demands, encompassing node addition, removal, and weight adjustments. This dynamic adaptation allows the network to real-time adjust to fluctuating input data, realizing adaptive computation. The innovation lies in the *dynamic* topology, contrasting with static structures or simplified models in existing neural-spike computing systems. By integrating parallel processing with machine learning optimization, our framework promises enhanced computational efficiency and resilience, representing a significant advancement over conventional neural-spike computing paradigms. The key mathematical framework revolves around representing the network topology as a graph (G = (V, E)), where V is the set of nodes (neurons) and E is the set of edges (connections) with associated weights. The dynamics of the network are governed by the following stochastic differential equations: d*s*i/dt = ∑j∈N(i) *w*ij *s*j + *f*i, where *s*i is the state of neuron *i*, *w*ij is the synaptic weight connecting neuron *i* to neuron *j*, *N(i)* is the set of neurons connected to neuron *i*, and *f*i represents a stochastic input or intrinsic noise. The learning process is formulated as a Markov Decision Process (MDP), and the policy is learned using reinforcement learning algorithms, aiming to maximize the expected reward. The core of the system can be represented as: R = ∑i αi *s*i, where αi is the activation function of neuron *i*. The system is designed to minimize the error between the output and the desired output, using a cost function: E = ∑i || *s*i - *t*i||2.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of neuromorphic computing to address the limitations of traditional von Neumann architectures in real-time image processing. The core argument presented is that the inherent bottleneck of separating memory and processing units in conventional computers significantly hinders the performance of computationally intensive image processing tasks. We propose leveraging neuromorphic chips and spiking neural networks (SNNs) as a solution. SNNs, inspired by the biological brain, offer the potential for drastically reduced power consumption and increased processing speeds compared to conventional digital systems. This work outlines the theoretical framework and key considerations for implementing image processing algorithms directly on neuromorphic hardware. Specifically, we explore the mapping of common image processing operations, such as edge detection and feature extraction, onto SNNs. The potential for achieving near-real-time performance with minimal energy expenditure is highlighted. Further research directions, including optimization strategies for SNN design and exploration of different neuromorphic architectures, are discussed.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of neuromorphic computing for event-driven data processing. Traditional computing architectures often struggle with the inherent inefficiencies of handling continuous data streams, leading to significant energy consumption. The core claim presented here is that mimicking the brain's event-driven neural processing offers a pathway to dramatically improved data processing efficiency and reduced energy expenditure. The proposed approach centers around constructing a neuromorphic system utilizing temporal circuits, leveraging the principles of event-driven signal transmission and parallel computation to achieve real-time data stream processing. This work outlines the theoretical framework and key design considerations for such a system, highlighting its advantages over conventional approaches. The research addresses the critical need for more energy-efficient data processing solutions, particularly in applications such as sensor networks, edge computing, and real-time analytics. The system's ability to react only to significant events within the data stream, rather than processing every element, is expected to yield substantial performance gains. Ultimately, this exploration contributes to the growing field of neuromorphic computing and its potential to revolutionize data handling paradigms. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel algorithm for dynamic topology optimization, leveraging the unique properties of quantum computing to dynamically adjust network parameters. The core mechanism utilizes quantum entanglement and superposition to efficiently compute and optimize the topology of a network, leading to improved performance compared to traditional optimization techniques. We propose a quantum-enhanced approach to network design, focusing on the dynamic adjustment of node and edge weights to achieve optimal network behavior. This work explores the potential of quantum computation to address challenges in network routing, congestion control, and resource allocation, offering a promising avenue for enhancing network efficiency and resilience. The proposed algorithm demonstrates improved results through extensive simulations and theoretical analysis, highlighting the potential for significant advancements in network management.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to neuro-symbolic reasoning by embedding temporal logic rules directly into the weights of a neural network. The core challenge in combining neural networks with symbolic reasoning lies in the absence of a shared representation language. Our method addresses this by creating a mechanism for the neural network to perform logical inference over time, guided by the embedded temporal logic rules. We propose a technique for mapping temporal logic formulas to the weights of a neural network, enabling the network to reason about temporal relationships and constraints. The resulting system exhibits improved reasoning capabilities compared to purely neural or purely symbolic approaches. This work provides a foundation for building more robust and explainable AI systems capable of handling complex, time-dependent reasoning tasks. The key innovation resides in the learned, differentiable mapping between temporal logic and neural network parameters, offering a pathway to seamless integration of these two powerful paradigms. The presented approach is evaluated conceptually, focusing on the architecture and embedding process, without empirical experimentation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of explainable artificial intelligence (XAI) techniques to the generation of synthetic data. The core claim is that XAI can significantly improve the quality and utility of synthetic data, ultimately facilitating the training of AI models while maintaining data privacy and ensuring desired data characteristics. The proposed mechanism involves leveraging XAI to analyze real-world data, identify key features, and generate synthetic data that accurately reflects these features. A key innovation lies in the ability to not only create synthetic data but to understand *why* that data was generated, leading to a higher degree of confidence in its validity and suitability for downstream AI model training. The generated synthetic data is evaluated for its effectiveness in mimicking the statistical properties of the original data, addressing concerns about data fidelity often associated with traditional synthetic data generation methods. This work contributes to the growing field of privacy-preserving data analytics and offers a pathway to more reliable and trustworthy AI model development.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to developing dynamic and explainable deep learning models. The core challenge in deploying deep learning systems is often their "black box" nature, hindering trust and adoption. This work addresses this issue by integrating Explainable Artificial Intelligence (XAI) techniques with Reinforcement Learning (RL). The resulting model, termed a Dynamic Explainable Deep Learning (DEDL) model, not only produces predictions but also provides a traceable explanation of its decision-making process. Crucially, the model incorporates a feedback loop driven by user input, allowing it to adapt its parameters and improve both its predictive accuracy and the clarity of its explanations over time. The system aims to create a truly interactive and understandable AI, shifting from opaque prediction to transparent reasoning. This paper details the architecture, the learning process, and the explanation generation strategies employed within the DEDL framework. The focus is on the design principles and the core algorithms, demonstrating a pathway towards more trustworthy and adaptable deep learning systems. The system's performance is evaluated based on a combination of predictive accuracy metrics and the subjective quality of the generated explanations. The key innovation lies in the continuous interplay between explanation and learning, fostering a truly dynamic and explainable AI.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
