Create 100qubits

100qubits

@@ -0,0 +1,81 @@
+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}")