Architecture. Execution. Sovereignty.

# Production quantum circuit - financial risk simulation
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit.library import RealAmplitudes
from qiskit.algorithms import VQE
from qiskit.primitives import Estimator
import numpy as np

class QuantumRiskSimulator:
    def __init__(self, num_qubits=16):
        self.num_qubits = num_qubits
        self.qr = QuantumRegister(num_qubits, 'q')
        self.cr = ClassicalRegister(num_qubits, 'c')
        self.circuit = QuantumCircuit(self.qr, self.cr)
        
    def build_risk_circuit(self, risk_factors):
        # Encode risk factors as quantum state
        self.circuit.initialize(
            self.encode_risk_factors(risk_factors), self.qr
        )
        
        # Apply quantum feature map
        for i in range(self.num_qubits):
            self.circuit.h(self.qr[i])
            self.circuit.rz(risk_factors[i] * np.pi, self.qr[i])
        
        # Entanglement layers for correlation modeling
        for layer in range(3):
            for i in range(self.num_qubits - 1):
                self.circuit.cx(self.qr[i], self.qr[i + 1])
                self.circuit.rz(
                    risk_factors[i] * risk_factors[i + 1] * np.pi / 2,
                    self.qr[i + 1]
                )
                self.circuit.cx(self.qr[i], self.qr[i + 1])
        
        # Variational quantum eigensolver for risk minimization
        ansatz = RealAmplitudes(self.num_qubits, reps=4)
        self.circuit.compose(ansatz, inplace=True)
        
        return self.circuit
    
    def simulate_risk(self, portfolio, shots=10000):
        # Execute on quantum hardware with error mitigation
        from qiskit_aer import AerSimulator
        
        simulator = AerSimulator(method='statevector')
        result = simulator.run(self.circuit, shots=shots).result()
        
        # Post-process with classical ML calibration
        risk_metrics = self.calibrate_results(result, portfolio)
        
        return risk_metrics

// Production Go service - real-time transaction processing
package main

import (
    "context"
    "fmt"
    "time"
    
    "github.com/gin-gonic/gin"
    "github.com/redis/go-redis/v9"
    "gorm.io/gorm"
    "golang.org/x/sync/errgroup"
)

type TransactionProcessor struct {
    db     *gorm.DB
    redis  *redis.Client
    kafka  *kafka.Producer
    config *Config
}

func (tp *TransactionProcessor) ProcessTransaction(
    ctx context.Context, 
    tx *Transaction,
) error {
    // Concurrent validation pipeline
    g, ctx := errgroup.WithContext(ctx)
    
    // Step 1: Fraud check with quantum circuit
    g.Go(func() error {
        return tp.quantumFraudCheck(ctx, tx)
    })
    
    // Step 2: Regulatory compliance validation
    g.Go(func() error {
        return tp.regulatoryCheck(ctx, tx)
    })
    
    // Step 3: Risk assessment
    g.Go(func() error {
        return tp.riskAssessment(ctx, tx)
    })
    
    // Wait for all validations
    if err := g.Wait(); err != nil {
        return fmt.Errorf("validation failed: %w", err)
    }
    
    // Atomic transaction processing
    err := tp.db.Transaction(func(tx *gorm.DB) error {
        if err := tx.Exec(
            "UPDATE accounts SET balance = balance - ? WHERE id = ?",
            tx.Amount, tx.FromAccount,
        ).Error; err != nil {
            return err
        }
        return nil
    })
    
    return err
}

# Production ML pipeline for real-time fraud detection
import mlflow
from feast import FeatureStore
from evidently.report import Report
import shap

class ProductionFraudDetection:
    def __init__(self, model_version: str = 'production'):
        self.fs = FeatureStore(repo_path="/feature_repo")
        self.model = self.load_production_model()
        
    def predict(self, transaction: Dict, features: pd.DataFrame) -> Dict:
        # Preprocess features
        processed = self.model['preprocessor'].transform(features)
        
        # Make prediction
        fraud_prob = self.model['model'].predict_proba(processed)[0][1]
        
        # Apply business rules
        if transaction['amount'] > 10000 and fraud_prob > 0.3:
            fraud_prob = min(fraud_prob * 1.5, 0.99)
        
        # Generate SHAP explanation
        explanation = self.generate_explanation(features, fraud_prob)
        
        return {
            'fraud_probability': float(fraud_prob),
            'prediction': fraud_prob > 0.5,
            'explanation': explanation,
        }
    
    def generate_explanation(self, features, probability):
        explainer = shap.TreeExplainer(self.model['model']._model_impl)
        shap_values = explainer.shap_values(features)
        
        return {
            'top_features': self.get_top_features(shap_values),
            'expected_value': float(explainer.expected_value[1]),
        }