main.py

##### QUANTUM ASCENDANT PROTOTYPES: ENHANCED QIP-X FRAMEWORK #####
# Developer: Reece Dixon
# Copyright (c) 2025 Reece Dixon. All rights reserved.
#
# NOTICE:
# This software, code, and all associated technologies (collectively referred to as "QIP-X Framework")
# are proprietary and confidential. Unauthorized reproduction, distribution, modification, or use
# of this software in any form is strictly prohibited unless explicitly authorized in writing by the developer.
#
# LEGAL DISCLAIMER:
# The QIP-X Framework is an advanced quantum-integrated AI system designed for research and specialized
# applications. Any unauthorized access, modification, or redistribution of this software may result
# in legal action. The developer, Reece Dixon, assumes no liability for any unintended consequences
# arising from the use or misuse of this technology. By accessing or using this software, you acknowledge
# and agree to comply with these terms.
#
# AUTOMATED LICENSE VERIFICATION:
# This software includes an integrity check mechanism to verify licensing compliance. Unauthorized usage
# will trigger security protocols and restrict access.

import os
import hashlib
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from qiskit import QuantumCircuit, execute, Aer
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.primitives import hashes
import time

### License Verification & Encryption Security ###
AUTHORIZED_USERS = ["Reece Dixon"]
LICENSE_HASH = hashlib.md5("Reece Dixon".encode()).hexdigest()

def verify_license(user):
    """Verifies if the current user is authorized to run this software."""
    hashed_user = hashlib.md5(user.encode()).hexdigest()
    if hashed_user != LICENSE_HASH:
        raise PermissionError("Unauthorized access detected. Use of this software is restricted.")

# Verify license for current user (example usage)
verify_license("Reece Dixon")

### 1. Quantum-Ascendant Cryptography (QAC) – Self-Adaptive & Secure Keys ###

def generate_quantum_entropy(bits=512, use_hardware=False, multi_qubit=False):
    """
    Generates quantum entropy using Qiskit. Supports both single-qubit and multi-qubit configurations.
    
    Parameters:
        bits (int): The number of bits of entropy to generate.
        use_hardware (bool): If True, use IBMQ hardware; otherwise, use simulator.
        multi_qubit (bool): If True, use a multi-qubit circuit for increased entropy.
    
    Returns:
        bytes: Entropy as a byte string.
    """
    if use_hardware:
        from qiskit.providers.ibmq import IBMQ
        IBMQ.load_account()
        provider = IBMQ.get_provider()
        backend = provider.get_backend('ibmq_qasm_simulator')
    else:
        backend = Aer.get_backend('qasm_simulator')
    
    num_qubits = 2 if multi_qubit else 1
    qc = QuantumCircuit(num_qubits, num_qubits)
    qc.h(range(num_qubits))
    qc.measure(range(num_qubits), range(num_qubits))
    
    bit_string = ""
    for _ in range(bits // num_qubits):
        job = execute(qc, backend=backend, shots=1)
        result = job.result()
        bit = list(result.get_counts(qc).keys())[0]
        bit_string += bit
    
    return int(bit_string, 2).to_bytes(bits // 8, byteorder='big')

def enhanced_chaotic_map(key, iterations=500, r=3.9999):
    """
    Applies chaotic transformations with adaptive bias correction to optimize entropy levels.
    
    Parameters:
        key (bytes): The input key bytes.
        iterations (int): Number of iterations for the chaotic map.
        r (float): Chaotic parameter.
        
    Returns:
        bytes: The transformed key.
    """
    key_int = int.from_bytes(key, byteorder='big')
    modulus = 2 ** (len(key) * 8)
    x = key_int / modulus
    # Adaptive bias correction factor based on the fractional part of x
    bias_correction_factor = 1.0 - (x % 0.01)
    for _ in range(iterations):
        x = (r * bias_correction_factor) * x * (1 - x)
    transformed_int = int(x * modulus) % modulus
    return transformed_int.to_bytes(len(key), byteorder='big')

### 2. Quantum-Assisted Simulated Annealing (Chrono-Intelligence Engine, CIE) ###

def quantum_random_qiskit(num_bits=16):
    """
    Generates a quantum-random number using Qiskit.
    
    Parameters:
        num_bits (int): Number of bits for the random number.
    
    Returns:
        float: A random number in the interval [0, 1).
    """
    backend = Aer.get_backend('qasm_simulator')
    qc = QuantumCircuit(num_bits, num_bits)
    qc.h(range(num_bits))
    qc.measure(range(num_bits), range(num_bits))
    job = execute(qc, backend=backend, shots=1)
    result = job.result()
    bit_str = list(result.get_counts(qc).keys())[0]
    rand_int = int(bit_str, 2)
    return rand_int / (2 ** num_bits)

def chrono_optimization(objective, initial_state, iterations=5000, temp=100.0, cooling=0.999):
    """
    Performs simulated annealing with quantum-assisted randomness for timeline optimization.
    
    Parameters:
        objective (function): The objective function to minimize.
        initial_state (float): The initial state for the optimization.
        iterations (int): Number of iterations.
        temp (float): Initial temperature.
        cooling (float): Cooling rate.
    
    Returns:
        float: The optimized state.
    """
    current_state = initial_state
    best_state = initial_state
    best_energy = objective(initial_state)
    for i in range(iterations):
        proposal = current_state + (quantum_random_qiskit() - 0.5) * 2
        energy = objective(proposal)
        if energy < best_energy or np.exp((best_energy - energy) / temp) > quantum_random_qiskit():
            best_state = proposal
            best_energy = energy
        cooling = max(0.995, 0.999 - (0.0001 * i / iterations))
        temp *= cooling
    return best_state

### 3. Hyper-Chaotic Neural Networks (HCNN) – Self-Healing AI Models ###

class DynamicActivation(nn.Module):
    """
    A dynamic activation function that switches between ReLU and a chaotic activation
    based on the stability of the input activations.
    """
    def __init__(self, iterations=10, r=3.9999):
        super(DynamicActivation, self).__init__()
        self.iterations = iterations
        self.r = r
    
    def forward(self, x):
        stability_metric = torch.std(x).item()
        adaptive_threshold = torch.mean(x).item() / 10  # Adaptive threshold based on mean activation
        if stability_metric > adaptive_threshold:
            return torch.relu(x)
        else:
            x = torch.sigmoid(x)
            for _ in range(self.iterations):
                x = self.r * x * (1 - x)
            return x

class DeepHCNN(nn.Module):
    """
    A deep hyper-chaotic neural network that utilizes dynamic activation functions
    for self-healing and adaptive learning.
    """
    def __init__(self, input_dim, hidden_dims, output_dim):
        super(DeepHCNN, self).__init__()
        layers = []
        prev_dim = input_dim
        for h_dim in hidden_dims:
            layers.append(nn.Linear(prev_dim, h_dim))
            layers.append(DynamicActivation(iterations=10, r=3.9999))
            prev_dim = h_dim
        layers.append(nn.Linear(prev_dim, output_dim))
        self.network = nn.Sequential(*layers)
    
    def forward(self, x):
        return self.network(x)

def train_hcnn(model, X, y, epochs=500):
    """
    Trains the deep hyper-chaotic neural network with gradient clipping for enhanced stability.
    
    Parameters:
        model (nn.Module): The neural network model.
        X (torch.Tensor): Input features.
        y (torch.Tensor): Target outputs.
        epochs (int): Number of training epochs.
    """
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    for epoch in range(epochs):
        optimizer.zero_grad()
        loss = criterion(model(X), y)
        loss.backward()
        optimizer.step()
        if epoch % 20 == 0:
            print(f"Epoch {epoch}, Loss: {loss.item()}")
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

### Example Usage ###

if __name__ == "__main__":
    # --- Quantum-Ascendant Cryptography ---
    # Generate quantum entropy (using simulator with multi-qubit support) and derive a secure key.
    quantum_seed = generate_quantum_entropy(bits=512, use_hardware=False, multi_qubit=True)
    secure_key = enhanced_chaotic_map(quantum_seed, iterations=500, r=3.9999)
    print("Quantum Ascendant Key (hex):", secure_key.hex())

    # --- Chrono-Intelligence Engine ---
    # Define an example objective function for timeline optimization.
    def objective_function(x):
        return (x - 5) ** 2 + np.sin(x) * 10

    optimal_timeline_state = chrono_optimization(objective_function, initial_state=0.0, iterations=5000, temp=100.0, cooling=0.999)
    print("Optimal Multiversal Timeline State:", optimal_timeline_state)

    # --- Hyper-Chaotic Neural Networks ---
    # Instantiate and train the Deep Hyper-Chaotic Neural Network.
    hcnn = DeepHCNN(input_dim=10, hidden_dims=[100, 100, 50], output_dim=1)
    X = torch.randn(500, 10)
    y = torch.randn(500, 1)
    train_hcnn(hcnn, X, y, epochs=500)