Experience Real world Application of Quantum Computing + Artificial Intelligence (NeuralLeadQHash) with Python Code

Github link :
https://github.com/simonjriddix/NeuralLeadQHash

Introduction:

Understanding SHA-256 and Its Limitations
SHA-256, a widely-used member of the SHA-2 family, has long been a cornerstone of cryptographic security. Designed by the NSA, this hash function transforms any input into a fixed 256-bit (32-byte) hash value. Its applications are vast, ranging from ensuring data integrity to verifying digital signatures and securing blockchain transactions.

However, despite its robust design, SHA-256 isn’t invincible. Emerging quantum computing threats, particularly Grover’s Algorithm, pose challenges. Grover’s Algorithm reduces the effective security of SHA-256 from 2^{256} to 2^{128} against brute-force attacks. While current quantum computers are not powerful enough to exploit this, the threat looms as technology evolves.

What Happens when you Sign-Up and Log-in On platform

When you sign up for a website, you create a username and password. Here’s what happens behind the scenes: The website doesn’t store your password directly. Instead, it scrambles it using a method called hashing. A random piece of data, called a salt, is added to your password before hashing. This makes it unique, even if someone else uses the same password. The website stores only the scrambled (hashed) version of your password, along with the salt, in its database.

When you log in:

You enter your password. The website scrambles your password again, using the same method and salt as before. It compares the new scrambled version with the one stored in the database.

If they match, you’re granted access.

This process keeps your actual password safe, even if someone breaks into the database. They’d only see the scrambled version, which is nearly impossible to reverse.

Databases like MySQL or MongoDB help store these hashed passwords securely. On top of that, some companies add another layer of protection by encrypting everything in the database, so it’s even harder for hackers to steal sensitive data.

The Problem: Cryptographic Challenges

Even without quantum computers, cryptographic systems face two significant threats:

Brute Force Attacks: With a 256-bit length, brute-forcing a SHA-256 hash requires 2^{256} operations, an infeasible task for classical systems.
Collision Attacks: SHA-256’s resistance to collision attacks comes with an attack complexity of 2^{128}. This high threshold makes finding two inputs producing the same hash impractical.
Quantum Threats: Quantum algorithms like Grover’s halve the effective strength, reducing 2^{256} to 2^{128}, a concerning possibility for future-proofing security.

NeuralLeadQHash — A Quantum-Inspired Hashing Algorithm

NeuralLead introduces NeuralLeadQHash, a revolutionary quantum-inspired algorithm. 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.

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.

Real-World Application: Password Security with NeuralLeadQHash

One critical application of NeuralLeadQHash is in securing passwords for databases and authentication systems. Below is an example in Python showcasing how NeuralLeadQHash enhances password security

Part 1: Integrating NeuralLeadQHash Library

The first part of the code sets up the foundation for using the NeuralLeadQHash hashing algorithm through Python’s ctypes module. It begins by importing necessary types like c_uint8 and c_size_t for handling binary data. An Enum-like class, ComputeStatus, defines constants representing the possible outcomes of hash operations: success, busy, or error.

Next, the NeuralLeadQHash shared library is loaded via ctypes.CDLL, allowing Python to access its hashing functions. The DirectComputeHashPointer function from the library is configured with specific input and output types. Finally, the compute_hash function accepts raw input data, prepares it as a C-compatible array, and calls the hashing function. The result is returned as a status code and the hash output. This setup seamlessly integrates an advanced cryptographic algorithm with Python, enabling secure hash computations.

import ctypes
from ctypes import c_uint8, c_size_t, POINTER
# Enum-like class to represent the return status of the hash computation function
class ComputeStatus(ctypes.c_int):
    HASH_SUCCESS = 0
    HASH_BUSY = 1
    HASH_ERROR = 2
# Load the NeuralLeadQHash shared library
hash_lib = ctypes.CDLL(r"C:\path\to\NeuralLeadQHash.dll")
# Define the argument types and return type for the hashing function
hash_lib.DirectComputeHashPointer.argtypes = [POINTER(c_uint8), c_size_t, POINTER(c_uint8)]
hash_lib.DirectComputeHashPointer.restype = ComputeStatus
def compute_hash(data):
    """Compute hash using NeuralLeadQHash."""
    length = len(data)
    data_array = (c_uint8 * length)(*data)
    output_array = (c_uint8 * 32)()
    status = hash_lib.DirectComputeHashPointer(data_array, length, output_array)
    return (status, bytes(output_array))

Part 2: Storing and Verifying Passwords

This section handles secure password storage and verification. The AskandStorePassword function prompts the user to input a password. It encodes the password into bytes, computes its hash using compute_hash, and stores the resulting hash in a file (password.db) as a hexadecimal string. By storing only the hash, the system ensures that even if the database is compromised, the original password remains unrecoverable.

The RetrieveAndCheckPassword function enables password verification. It reads the stored hash from the password.db file and prompts the user to enter their password for login. This input is encoded and hashed using the same compute_hash function. The resulting hash is then compared to the stored hash. If they match, it confirms the password is correct; otherwise, it rejects the login attempt. This approach provides robust security by ensuring passwords are never stored in plaintext, leveraging hashing to verify user credentials without exposing sensitive data.

def AskandStorePassword():
    """Prompts user to store a hashed password."""
    NewPassword = input("Write your new password: ")
    EncodedNewPassword = NewPassword.encode('utf-8')
    (status, HashedOutput) = compute_hash(EncodedNewPassword)

    with open("password.db", "w", encoding="utf-8") as file:
        file.write(HashedOutput.hex())

def RetrieveAndCheckPassword():
    """Retrieves stored hashed password and verifies user input."""
    with open("password.db", "r", encoding="utf-8") as file:
        HashedPasswordFromDB = file.read().strip()

    PasswordToBeChecked = input("Type your password to login: ")
    EncodedPasswordToBeChecked = PasswordToBeChecked.encode('utf-8')
    (statusCheck, HashedPasswordOutput) = compute_hash(EncodedPasswordToBeChecked)

    if HashedPasswordFromDB == HashedPasswordOutput.hex():
        print("Your password is correct.")
    else:
        print("Your password is not correct.")

Part 3: Main Program Execution

The final part orchestrates the process by calling AskandStorePassword to set up a password and RetrieveAndCheckPassword to validate it. This ensures the system secures and verifies user credentials efficiently, providing a practical example of real-world password management using advanced hashing algorithms.

if __name__ == '__main__':
    AskandStorePassword()
    RetrieveAndCheckPassword()

Conclusion

Redefining Security for a Quantum Future
As the world braces for quantum computing, cryptography must evolve. NeuralLeadQHash offers a proactive solution by blending the strengths of quantum-inspired technology and AI. Its high entropy, resistance to attacks, and computational efficiency position it as a game-changer in fields like blockchain and secure authentication.

At NeuralLead, we’re not just adapting to the future — we’re shaping it. Whether you’re securing passwords, protecting blockchain networks, or exploring next-gen cryptography, NeuralLeadQHash ensures you’re always a step ahead.

Let’s embrace the quantum era together.