The author expresses gratitude to OpenAI and the development team for creating the tool that played a crucial role in the development of this library. The GPT-3 technology developed by OpenAI was used as an auxiliary tool for text verification and adaptation, as well as for correcting JS code. The AI helped correct punctuation, stylistic, and grammatical errors, as well as providing suggestions to improve the quality of the material.
The author carefully reviewed all suggested changes and adapted them at their discretion, taking full responsibility for the modifications and content of the project. All edits made using GPT-3 technology were reviewed and approved by the author, who assumes full responsibility for the final result.
QuantNeuroChaOSys.js (Quantum Neuro Chaos System) is a library for modeling multidimensional quantum neural networks. It uses elements of quantum chaos to create nonlinear, high-dimensional models that can learn and adapt to input data. The library offers flexible settings for fine-tuning neuro-quantum nodes and data processing algorithms.
Neuro-quantum nodes in the context of this library represent elements of a multidimensional neural network that can change their state based on quantum principles. Each node has a state described by multiple parameters (e.g., weights and entanglement levels). Unlike traditional neural networks, where nodes interact with others based on deterministic rules, neuro-quantum nodes use random processes inspired by quantum effects such as superposition and interference.
The core algorithm of the library involves the creation and interaction of these quantum nodes. Each node has its own state, which can be altered based on its internal weight and environment, as well as random processes. A key feature is the use of quantum entanglement, where the interaction of one node with another occurs not only through direct adjacency but also through more complex interactions with "neighbors" in multidimensional space. This allows the proposed neuro-quantum model of the network to adapt to data with much greater flexibility and power.
The model used in the QuantNeuroChaOSys.js library is based on quantum principles such as superposition, interference, and entanglement to create a neural network. Unlike traditional neural networks, where nodes interact based on predefined weights, this model uses random processes similar to the principles of quantum mechanics to predict the behavior of the network. Below are the main components and algorithms underlying the operation of the library:
Quantum nodes in this model are created considering their state in multidimensional space. Each node is assigned a state that is a vector with a dimensionality equal to the number of dimensions specified by the user. This state is updated using quantum effects like superposition and interference.
The node's state is updated as follows:
statenew = Σ (stateneighbor * weightneighbor) + random_factor
Where:
Entanglement is a phenomenon where the state of one node can be linked to the state of other nodes in the network. In the context of neural networks, this means that the state update of one node will depend on the states of other nodes in the network, even if they are far apart.
Thus, nodes in this network interact not only with their immediate neighbors but also with more distant nodes, significantly enhancing the network's learning and adaptation. Entanglement also increases the likelihood of complex patterns and non-trivial solutions for complex problems.
Each update of a node's state occurs over several iterations. This allows the neural network to gradually adapt to data and improve its results as it processes them. It is important that the number of iterations and their impact on the results can be adjusted.
statenew = stateold + Σ (weightneighbor * stateneighbor) + random_factor
The system uses pseudo-random numbers generated through quantum chaos to model random processes that affect the states of nodes. This introduces an element of uncertainty and complexity in data processing, allowing the network to find more intricate and adaptive solutions.
random_value = sin(Σ (statenode * weightnode) % PI)
Where:
Example: Suppose there are three nodes in the network with states state_1 = 0.5, state_2 = 0.8, state_3 = 0.3 and corresponding weights weight_1 = 1.2, weight_2 = 0.7, weight_3 = 0.5. Then:
Σ (statenode * weightnode) = (0.5 * 1.2) + (0.8 * 0.7) + (0.3 * 0.5) = 0.6 + 0.56 + 0.15 = 1.31
random_value = sin(1.31 % π) ≈ sin(1.31) ≈ 0.966
The final value random_value ≈ 0.966 will be used to update the state of the nodes.
The goal of the project is to create a library that does not merely model standard neural networks, but offers an alternative approach using elements of quantum chaos. This allows the creation of networks that can tackle more complex tasks, such as predictions under uncertainty, modeling dynamic processes with high levels of chaos and uncertainty, and processing multidimensional data with a unique structure.
The concept of the project is based on the idea of using quantum principles to extend the capabilities of traditional neural networks. In the traditional approach to neural networks, we are limited by linear mathematics and deterministic algorithms. However, in nature and in the universe, there are often processes that are nonlinear and random. The goal of the project is to create a model that can effectively work with such processes and use them as tools to solve real-world problems.
The philosophy of this project lies in exploring new pathways and methods that open up quantum mechanisms for data processing. This pursuit aims to create flexible, adaptive, and powerful tools for scientific research, artificial intelligence, and solving unconventional problems that cannot be addressed by traditional methods. This research is at the intersection of neuroscience, quantum physics, and chaos theory, offering a completely new perspective on the operation of artificial neural networks.
QuantNeuroChaOSys.js is a library for modeling multidimensional quantum neural networks. It uses elements of quantum chaos to create nonlinear, high-dimensional models that can learn and adapt to input data. The library offers flexible settings for fine-tuning quantum-neural nodes and data processing algorithms.
Quantum-neural nodes in this context are elements of a multidimensional neural network that can change their state according to quantum principles. Each node has a state that is described by many parameters (e.g., weights and levels of entanglement). Unlike traditional neural networks, where nodes interact with each other based on deterministic rules, quantum-neural nodes use random processes inspired by quantum effects such as superposition and interference.
The main algorithm of the library involves the creation and interaction of these quantum nodes. Each node has its own state, which can be altered based on its internal weight, the environment, and random processes. An important feature is the use of quantum entanglement, where the interaction of one node with another occurs not only through direct adjacency but also through more complex interactions with "neighbors" in a multidimensional space. This allows the proposed quantum-neural model to adapt to data with much greater flexibility and power.
To use the library, include it in your project and call the main function QuantumNeuroChaos.process(). This function accepts input data and a set of parameters for processing.
QuantumNeuroChaos.process(input, options);Where:
input — data to be processed. This is the input value, which can be a string, number, array, or object. The library automatically converts the data into a string for further processing.options — an optional object with settings. If not provided, default values are used (see below).options:The number of nodes in the network. This defines the complexity of the model. More nodes increase computational costs. Default: 8.
The number of iterations for processing nodes. Increasing the number of iterations allows the model to better explore interactions between nodes. Default: 5.
The number of dimensions for the nodes' states. This defines the dimensionality of the space in which computations occur. High values may be useful for working with multidimensional data. Default: 4.
Enabling randomness in the computation process. If true, randomness is introduced to model chaos. Default: false.
A numerical feedback value for model adaptation. It is used for dynamically adjusting node weights. If not specified, feedback is not applied. Default: null.
The library supports the following data types for the input parameter:
,) and then processed.JSON.stringify.The result of calling the process function is a numerical value, representing the final computation of the model. This value can be used for:
// Example of using the library
const result = QuantumNeuroChaos.process("Example data", {
numNodes: 10,
iterations: 7,
dimensions: 5,
randomize: true,
feedback: 0.1
});
console.log(result); // Final computed valueThe QuantNeuroChaOSys.js library offers the ability to model quantum neural networks, making it suitable for a wide range of complex tasks. Unlike classical neural networks, it uses elements of quantum chaos and randomness, potentially enabling effective work in the following areas:
Thanks to its ability to process data with elements of randomness and entanglement, the library is ideal for tasks related to prediction under limited or incomplete information. Examples include:
The use of multidimensional states allows the modeling of dynamic processes that cannot be accurately described using linear methods. Examples include:
The library can be used to create new types of AI models that combine randomness and determinism. Examples include:
The library opens new possibilities for scientific research at the intersection of quantum physics, chaos theory, and neural networks. It can be used for:
The library continues to develop, and its use may expand into new areas, including quantum computing and hybrid models combining classical and quantum approaches.
seed string. It uses elements of quantum chaos to create random values.
This software, QuantNeuroChaOSys.js (hereinafter referred to as "Software"), is distributed under the license described below. The use of the Software is only allowed under the terms of this agreement.
The Software is provided for free use, copying, adaptation, and modification. Users are free to modify the Software for their own needs, provided the following conditions are met:
The Software is provided "as is," without any warranties, including but not limited to, warranties of merchantability, fitness for a particular purpose, or non-infringement. The original author or copyright holder is not responsible for any damages, losses, or issues arising from the use of the Software, including but not limited to data loss, system damage, or malfunction. The user uses the Software at their own risk.
The user may not:
All rights to the Software are owned by QuantumNeuroChaosSystem HomeDev by I. "Calc" Petrov. The user acknowledges that all copyrights and other intellectual property rights to the Software remain with the owner.
This document is a legally binding agreement between you (the user) and the copyright holder of the Software. If any provisions of this agreement are found to be invalid or unenforceable, the remaining provisions will remain in effect.
This library was developed by the author from scratch without using external libraries or borrowing code from third-party sources. The author did not base the development on existing solutions but respects the intellectual property of other developers.
If any elements of the library accidentally overlap with existing solutions, this is an unintentional coincidence, and the author is willing to acknowledge the priorities of the original developers.
For inquiries, please email: quantumneurochaossystem@tuta.io