100qubits.js

import numpy as np
from qiskit import Aer
from qiskit.algorithms import QAOA
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.optimization import QuadraticProgram
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from torch import cuda

# Quantum Optimization (MaxCut Problem for task optimization)
def create_maxcut_problem(num_nodes, edges, weights):
    qp = QuadraticProgram()
    for i in range(num_nodes):
        qp.binary_var(f'x{i}')
    for i, j in edges:
        weight = weights.get((i, j), 1)
        qp.minimize(constant=0, linear=[], quadratic={(f'x{i}', f'x{j}'): weight})
    return qp

def quantum_optimization(qp):
    backend = Aer.get_backend('statevector_simulator')
    qaoa = QAOA(quantum_instance=backend)
    optimizer = MinimumEigenOptimizer(qaoa)
    result = optimizer.solve(qp)
    return result

# Load Hugging Face GPT-2 model for text generation
def load_hugging_face_model():
    model_name = 'gpt2'
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_name)
    return model, tokenizer

# Quantum-enhanced Machine Learning Model
def quantum_machine_learning_model(X_train, y_train, X_test, y_test):
    # Classical SVM model as baseline
    from sklearn.svm import SVC
    clf = SVC(kernel='linear')
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    
    # Quantum optimization (MaxCut Problem)
    maxcut_problem = create_maxcut_problem(4, [(0, 1), (1, 2), (2, 3), (3, 0)], {(0, 1): 1, (1, 2): 1, (2, 3): 1, (3, 0): 1})
    quantum_result = quantum_optimization(maxcut_problem)

    return score, quantum_result

# Text generation with Hugging Face GPT-2
def generate_text(prompt, model, tokenizer, max_length=100):
    inputs = tokenizer.encode(prompt, return_tensors='pt')
    outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, top_p=0.92, temperature=1.0)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Uncensored Bot with Quantum Optimization for Efficiency
def quantum_uncensored_bot():
    # Generate synthetic classification data
    X, y = make_classification(n_samples=100, n_features=2, n_classes=2, random_state=42)
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    
    # Run quantum-enhanced machine learning (optimization + SVM)
    accuracy, quantum_result = quantum_machine_learning_model(X_train, y_train, X_test, y_test)

    # Load the Hugging Face GPT-2 model
    model, tokenizer = load_hugging_face_model()

    # Generate uncensored text
    prompt = "This is a sample input to the uncensored AI."
    generated_text = generate_text(prompt, model, tokenizer)

    return accuracy, quantum_result, generated_text

# Execute the bot
accuracy, quantum_result, generated_text = quantum_uncensored_bot()

# Print results
print(f"Accuracy: {accuracy}")
print(f"Quantum Result: {quantum_result}")
print(f"Generated Text: {generated_text}")