Quantum Computing, Quantum Annealing, Optimization, Wave Function, Artificial Intelligence.
= np.random.randn(252, n_assets) expected_returns = np.mean(historical_returns, axis=0) cov_matrix = np.cov(historical_returns.T) # Step 2: Define the objective function def portfolio_variance(weights): return weights.T @ cov_matrix @ weights constraints = ({'type': 'eq', 'fun': lambda weights: