# 传统设计流程伪代码
def traditional_design():
    initial_topology = manual_design()
    while not meet_performance_criteria:
        manual_adjustment()
        simulation()
    return final_design

# AI优化流程伪代码
def ai_optimized_design():
    generate_population(topology_candidates)
    evaluate_fitness(performance_metrics)
    while not convergence:
        selection()
        crossover()
        mutation()
        evaluate_fitness()
    return optimal_topology

% MATLAB混合优化示例
function [best] = hybrid_optimization()
    % 初始化种群
    pop = initialize_population();

    % NSGA-II主循环
    for gen = 1:max_gen
        offsprings = crossover(pop);
        offsprings = mutate(offsprings);
        pop = [pop; offsprings];

        % 多目标评估
        fitness = evaluate(pop);

        % 非支配排序
        [fronts, crowding] = nsga2_sort(fitness);

        % 选择下一代
        pop = select(pop, fronts, crowding);
    end

    % 强化学习微调
    best = rl_finetune(pop);
end

module power_monitor(
    input clk,
    input rst,
    output reg [31:0] power_reading
);
    reg [31:0] current_sample;

    always @(posedge clk or posedge rst) begin
        if (rst) begin
            current_sample <= 32'h0;
            power_reading <= 32'h0;
        end else begin
            current_sample <= adc_read();
            power_reading <= calculate_power(current_sample);
        end
    end
endmodule

from qiskit import QuantumCircuit
def quantum_topology_search():
    qc = QuantumCircuit(5)
    for i in range(5):
        qc.h(i)
    qc.measure_all()
    result = execute(qc).result()
    return decode_topology(result)