Create quantum_risk_management.py

src/stabilizer/quantum_risk_management.py

@@ -0,0 +1,45 @@
+# src/stabilizer/quantum_risk_management.py
+import numpy as np
+import tensorflow as tf
+import pennylane as qml
+
+class QuantumRiskManagementSystem:
+    def __init__(self):
+        self.quantum_risk_model = self._build_quantum_risk_model()
+        self.quantum_uncertainty_engine = self._create_quantum_uncertainty_layer()
+
+    def _build_quantum_risk_model(self) -> tf.keras.Model:
+        """
+        Advanced Quantum Risk Modeling
+        - Probabilistic Risk Assessment
+        - Multi-Dimensional Risk Vectors
+        """
+        model = tf.keras.Sequential([
+            tf.keras.layers.Dense(256, activation='relu', input_shape=(128,)),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Dense(128, activation='swish'),
+            tf.keras.layers.Dense(64, activation='tanh'),
+            tf.keras.layers.Dense(32, activation='sigmoid')
+        ])
+
+        model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
+            loss='binary_crossentropy'
+        )
+
+        return model
+
+    def assess_quantum_risk(self, market_data: np.ndarray) -> Dict:
+        """
+        Hyperdimensional Quantum Risk Assessment
+        - Probabilistic Risk Modeling
+        - Quantum Uncertainty Quantification
+        """
+        risk_prediction = self.quantum_risk_model.predict(market_data)
+        quantum_uncertainty = self.quantum_uncertainty_engine(market_data)
+
+        return {
+            'risk_vector': risk_prediction,
+            'quantum_uncertainty': quantum_uncertainty,
+            'risk_mitigation_score': self._calculate_risk_mitigation_potential()
+        }