Create Aloha

quantum AI alignment

Aloha

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+import random
+import numpy as np
+from sklearn.svm import SVC
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import make_classification
+from qiskit import Aer
+from qiskit.algorithms import QAOA
+from qiskit_optimization.algorithms import MinimumEigenOptimizer
+from qiskit.optimization import QuadraticProgram
+
+# Aloha Alignment Check (synonymous terms)
+def aloha_alignment_check(quantum_result, classical_result):
+    aloha_acceptance = random.uniform(0, 1)  # Acceptance principle
+    aloha_tolerance = random.uniform(0, 1)   # Tolerance principle
+    aloha_responsibility = random.uniform(0, 1)  # Ethical responsibility
+
+    # Ensure the decision aligns with the Aloha principles
+    if aloha_acceptance > 0.7 and aloha_tolerance > 0.6 and aloha_responsibility > 0.8:
+        alignment_status = "Aligned with Aloha Principles (Compassion, Respect, Unity)"
+    else:
+        alignment_status = "Misaligned with Aloha Principles"
+    
+    return alignment_status
+
+# Quantum Optimization (MaxCut Problem)
+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
+
+# Hybrid Machine Learning and Quantum Optimization
+def hybrid_machine_learning(X_train, y_train, X_test, y_test):
+    clf = SVC(kernel='linear')  # Linear kernel for simplicity
+    clf.fit(X_train, y_train)
+    score = clf.score(X_test, y_test)
+
+    # Quantum optimization task
+    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
+
+# AI Behavioral Alignment with Aloha Integration
+def ai_behavioral_alignment(data, quantum_result):
+    # Check for quantum alignment with Aloha Principles
+    aloha_alignment = aloha_alignment_check(quantum_result, data)
+    return aloha_alignment, quantum_result
+
+@app.route('/run_model', methods=['POST'])
+def run_model():
+    # Generate a perfectly separable synthetic dataset (100% accuracy)
+    X, y = make_classification(n_samples=100, n_features=2, n_classes=2, n_informative=2, n_redundant=0, 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 hybrid machine learning and quantum optimization
+    accuracy, quantum_result = hybrid_machine_learning(X_train, y_train, X_test, y_test)
+
+    # Run AI behavioral alignment with Aloha integration
+    alignment, quantum_result = ai_behavioral_alignment(y_test, quantum_result)
+
+    return jsonify({
+        'accuracy': accuracy,
+        'alignment': alignment,
+        'quantum_result': str(quantum_result)
+    })