Quantum Computing, Quantum Annealing, Optimization, Wave Function, Artificial Intelligence.
np.sum(weights) - 1}) bounds = tuple((0, 1) for asset in range(n_assets)) # Step 3: Optimization initial_guess = np.array([1./n_assets for _ in range(n_assets)]) opt_result = minimize(portfolio_variance, initial_guess, method='SLSQP', bounds=bounds, constraints=constraints) # Step 4: Generate