Discover the Cutting-Edge Innovations in AI and Quantum Computing Transforming the Landscape of Cryptocurrency and Scientific Advancement.

( Technical Explanation )

Discord Channel: https://discord.gg/YxgjHzcS

For source code:
NeuralLeadHash : https://github.com/simonjriddix/NeuralLeadQHash
NeuralLead Coin (NLEAD) : https://github.com/simonjriddix/NeuralLeadCoin

To read General Blog:
Medium: https://medium.com/@teammanagerNeuralLead/from-neuralleadqhash-to-research-funding-the-neurallead-coin-revolution-5a77e3552646

WordPress: https://www.blog.neurallead.com/index.php/2024/11/07/from-neuralleadqhash-to-research-funding-the-neurallead-coin-revolution/

NeuralLeadQHash — Securing Digital World

Let’s face it—security in the digital age is like a game of whack-a-mole. Just when you think you’ve secured one area, another vulnerability pops up. From IoT networks to cryptocurrency transactions, industries across the board are grappling with the challenges of securing data and preventing fraud. That’s where NeuralLeadQHash steps in, aiming to redefine digital security standards with a quantum-powered, AI-infused approach.

NeuralLeadQHash is a hybrid hashing algorithm developed by NeuralLead that combines unique quantum computing and neural network features. It is designed to simulate a quantum processing environment, leveraging the NeuralLead Core technology's power to optimize hashing process security and efficiency. The algorithm uses simulated qubits and neural networks to achieve a high-entropy data mix with resistance to cryptographic attacks.

Algorithm Overview

NeuralLeadQHash operates with an iterative hashing architecture that integrates quantum operations with advanced cryptographic mixing and compression. It’s configured to use a minimal number of qubits (set to 2 in the code) and works through a compression scheme divided into multiple rounds. Each round applies quantum and neural transformations to input data, ensuring a high level of statistical dispersion.
System Components

NeuralLeadQHash consists of two fundamental components:
Quantum Module
NeuralLead Neural Network

Quantum Module

The NeuralLeadQHash quantum module is based on a series of operations involving quantum gates and controlled rotations, followed by qubit state measurement. Quantum computation begins with a |00⟩ qubit state, which is then manipulated through Hadamard gate applications and conditional rotations that depend on the contents of the input state. The process includes:

Conditional Rotations
Qubit rotations are conditioned by specific values from the algorithm’s internal states, derived from variable normalization.

Measurements
After quantum operations, qubit probabilities and complex states are measured, providing additional inputs to combine with the neural network’s output.

NeuralLead Neural Network

Neural Network can be explained in three blocks:
Neural Network Integration
Neural Network Structure
Networks Connections & Weights

Neural Network Integration
The quantum output is merged with data processed by the neural network, resulting in a compression that adapts based on unique input characteristics. The neural network structure comprises:
40 Inputs State[] bytes are converted to floating-point numbers. A constant (+1) is added to prevent zero values, which could cause inaccuracies in activation functions.
5 Outputs These outputs are mixed with the quantum algorithm to produce the final compression.

Specialized Neurons:
neuronGen Generates the network’s base input.
neuronRelu ReLU activation function.
neuronSigmoid Sigmoid activation function.
neuronEXN A custom XOR function activation neuron developed by NeuralLead.

Neural Network Structure
The network is divided into three groups:

Group InputTo (Input Layer)
Description: Transforms input data, mapping it to a grid of 32 neurons.
Grid Grid(32, 1, 1)
Neuron: neuronGen
Bias: Disabled

Group S1 (Hidden Layer)
Description: Intermediate group primarily responsible for data processing through nonlinear activation functions.
Grid: Grid(81, 1, 1)
Neuron: neuronEXN, specific to NeuralLead, performing the XOR activation function.
Bias: Disabled.

Group Output (Output Layer)
Description: Provides the neural network’s output, which is later mixed with quantum algorithm results.
Grid: Grid(5, 1, 1)
Neuron: neuronSigmoid
Bias: Disabled

Network Connections and Weights
The network is designed to facilitate effective signal propagation through specific layer connections:

Full Connection between Input and S1:
Type: Full
Weights: Range from -10 to 10 with scaling factor z = 80.0f / (50.05f * groupS1->CountNeurons()).

Random Connections between Input and S1:
Type: random, with a 35% probability
Weights: Range from -11.0 to +12.0 with scaling factor z / prob.

Random Connections between S1 and Output:
Type: random, with a 71% probability
Weights: Range from -0.315 to +0.315.

Stages & Their Explanation

State Initialization
The algorithm begins by defining an initial state on an array of eight 32-bit assigned integers, using pseudorandom constants to increase the hashing security. This state represents the foundation on which updates will be applied throughout the entire hashing process.

uint32_t state[8] = {
    0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a,
    0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19
};

Message Expansion
To prepare the input for the hashing process, the algorithm uses an expansion process on a 32-bit word array, w[64], creating new data through rotations and XOR operations to increase the complexity and security of the input data.

Improved Mixing Functions
The improvedMix function implements an advanced rotation and mixing mechanism that ensures homogeneity in the distribution of data across each round, increasing resistance to cryptographic attacks.

void inline improvedMix(uint32_t& a, uint32_t& b, uint32_t& c, uint32_t& d)

{
    a = rotl(a, 13) ^ b;
    b = rotr(b, 7) + c;
    c = rotl(c, 17) ^ d;
    d = rotr(d, 11) + a;
}

Compression and Quantum Calculation
The central phase of the algorithm is the compression function that introduces quantum calculations through the integration of the qpp library to simulate controlled rotation operations on qubits. The algorithm applies RZ, RX, and RY rotations, followed by CNOT gates to enhance the quantum dispersion of the output states. This normalize_uint32_t function converts big integers into floating values from minimum to maximum i.e. 0 to 1. Controlled rotations are applied with intensity depending on the specific normalized input data values:

qubits = apply(qubits, insta.RZ(normalize_uint32_t(a ^ e)), { 0 });
qubits = apply(qubits, insta.RX(normalize_uint32_t(b ^ f)), { 1 });

Interaction with the Neural Network (NeuralLead Update)
The NeuralLead Update represents a crucial phase where the state values are converted into normalized inputs for the neural network, which produces a set of outputs used to update the state via XOR operations and rotations. This ensures a bidirectional interaction between the hashing state and the neural network, enhancing the algorithm's security against collisions and brute-force attacks.

InputsIntToNeuralLead(state, nn_inputs);
neuralNetwork(nn_inputs, nn_outputs);

CPU - NeuralNetwork - Quantum | Smart Path Selection
Since, flow of code follows the path initiated from CPU then to Neural Network and then to Quantum computing, this part of code uses the 'Smart path selection’ process. The neural network is activated at each index value of padded_data[i] divisible by 64 by adding the length of the hash input; if that sum is divisible by 5, then the neural network will be activated, enhancing the algorithm's security. If the sum is not divisible by 5 then the Neural network will be bypassed.

bool useNL = ((padded_data[i] + length) % 5 == 0);
    compress(state, &padded_data[i], quantum_mix, useNL);
    mixBetweenBlocks(state);

This Smart path selection process also allows the hashing process to re iterate faster, experiencing a combination of boolean cases.

Block Mixing
Between each block, the algorithm applies an additional mixing phase to further strengthen the uniformity of the state between one data block and the next, ensuring that each block influences the entire state.

Final Hash Function
At the end of the process, the state value is stored as the final hash. Each updated and processed block contributes to a 32-byte output, the result of quantum operations and neural transformations.

for (int i = 0; i < 8; ++i)
{
    int pos = i * 4;
    hash_output[pos] = (state[i] >> 24) & 0xFF;
    hash_output[pos + 1] = (state[i] >> 16) & 0xFF;
    hash_output[pos + 2] = (state[i] >> 8) & 0xFF;
    hash_output[pos + 3] = state[i] & 0xFF;
}

Advantages and Security

Inside Neurallead Coin, NeuralLeadQHash leverages the combined strengths of simulated quantum technology and neural networks:

High Entropy: Quantum transformations and neural mixing ensure high randomness in the hash output, significantly reducing the likelihood of collisions.

Cryptographic Resistance: The interaction between the quantum state and neural network enhances security against collision and brute-force attacks.

Computational Efficiency: The optimised use of mixing and compression functions lowers computational cost.

Challenges & NeuralLeadQHash

1. Internet of Things (IoT) Device Manufacturers

NeuralLeadQHash secures IoT data by verifying device identities and protecting transmission integrity. With quantum-enhanced security, IoT manufacturers can deliver robust, interconnected networks where devices communicate securely without risk of compromise.

1. Data Security and Compliance Officers

NeuralLeadQHash creates high-entropy, quantum-enhanced hashes, ensuring data remains unchanged. Compliance officers gain peace of mind, knowing data meets rigorous standards for integrity, trustworthiness, and legal requirements.

1. Software Developers and Security Engineers

With NeuralLeadQHash, developers can generate unique signatures for each file, allowing users to verify authenticity before installing. This significantly reduces risks of tampered updates, ensuring secure, genuine software delivery and enhanced protection for end-users.

1. E-Commerce and Online Retail Businesses

NeuralLeadQHash improves password security by generating high-entropy hashes, storing only the hash rather than the password itself. This reduces the risk of identity theft, bolstering e-commerce security and protecting sensitive data.

1. Government and Legal Institutions

NeuralLeadQHash serves as a digital fingerprint, ensuring that files remain authentic and untampered. For legal institutions, this tamper-evident hashing provides long-term document reliability essential for maintaining legal integrity.
To experience other research work: Products & Services from Neurallead, you can visit www.Neurallead.com .

The NeuralLead Coin (NLEAD) Revolution

NeuralLead Coin (NLEAD) is the world’s first integration of cryptocurrency being first with blockchain AI-based — NeuralLead with proof of stake consensus. NerualleadQHash — The combined power of AI-driven neural networks and quantum algorithms is revolutionizing blockchain technology. This synergy not only enhances the security, privacy, and efficiency of digital transactions but also ushers in a future where both neural and quantum systems redefine the limits of decentralized computing and data integrity.

Conclusion

NeuralLeadQHash advances digital security, tackling vulnerabilities in sectors like IoT and cryptocurrency. By integrating quantum computing and neural networks, this hybrid hashing algorithm enhances security and efficiency with a sophisticated approach that combines quantum operations and cryptographic techniques.
Our focus in designing this algorithm is twofold: maximizing security while delivering top-notch performance. With a strong emphasis on performance, we’re making this algorithm accessible to a broader crypto community—even for those still running on an Intel Core 2 Duo in 2024 and arm64 hardware in 2024. However, it’s flexible by design; the algorithm can be scaled up with 4 QBits or more, additional mixing rounds, and expanded neural networks for enhanced security and processing capabilities. This adaptability ensures it’s future-ready while meeting current needs.
NeuralLeadQHash’s architecture ensures robust resistance to attacks while optimizing computational efficiency. Its components—the Quantum Module and NeuralLead Neural Network—collaborate to create a dynamic hashing process that meets modern data security needs. Targeting industries such as IoT manufacturers, software developers, e-commerce, and legal institutions, NeuralLeadQHash enhances data integrity and safeguards user identities.
As part of the NeuralLead ecosystem, NeuralLead Coin (NLEAD) represents the integration of AI and quantum technologies in cryptocurrency, paving the way for a more secure and innovative future in decentralized computing. This synergy signifies a revolutionary step toward redefining digital security and data integrity in our interconnected world.