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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}") |