1.# 导入所需的库2.import dwave.cloud3.import dwavebinarycsp4.from dwave.system.samplers import DWaveSampler5.from dwave.system.composites import EmbeddingComposite6.import numpy as np7.8.# 定义信用评分卡的个数和阈值的个数9.num_cards = 10010.num_thresholds = num_cards - 111.12.# 定义信用评分卡和阈值的权重13.card_weights = np.random.rand(num_cards)14.threshold_weights = np.random.rand(num_thresholds)15.16.# 定义约束条件17.csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY)18.for i in range(num_cards):19.    for j in range(num_thresholds):20.        csp.add_constraint(lambda x, i=i, j=j: (x[i] + x[i+1] - 2*x[num_cards+j]) <= 0, [i, i+1, num_cards+j])21.22.# 将约束条件转换为 QUBO 形式23.bqm = dwavebinarycsp.stitch(csp)24.25.# 在 D-Wave 上求解 QUBO 模型26.sampler = EmbeddingComposite(DWaveSampler(solver={"qpu": True}))27.response = sampler.sample(bqm, num_reads=1000)28.29.# 输出结果30.print(response)