量子计算-P3.PyQUBO使用-Max Cut 问题

from pyqubo import Array, Binary
import networkx as nx
import itertools 
import neal






def random_graph(node_num,p = 0.3):
    G = nx.Graph()      
    H = nx.path_graph(node_num)  
    G.add_nodes_from(H)
    comb = list(itertools.combinations(range(node_num),2))
    for e in comb:
        probability =random.random()#生成随机小数
        if(probability<p):      #如果小于p
            G.add_edge(e[0],e[1])        
    return G
def binary_array(G):
    b_arr = Array.create('b', shape=(len(G.nodes), 1), vartype='BINARY')
    H = []
    for e in list(G.edges):
        H.append(b_arr[e[0]]+b_arr[e[1]]-2*b_arr[e[0]]*b_arr[e[1]])
    H_t = -sum(H)
    return H_t
def solver(H_t):
    # 编译
    model = H_t.compile()
    # 模型返回QUBO
    qubo, offset = model.to_qubo()
    print(qubo)        
    # 创建SA求解器
    sa = neal.SimulatedAnnealingSampler()
    # 采样
    sampleset = sa.sample_qubo(qubo)
    # 解码
    decoded_samples = model.decode_sampleset(sampleset)
    best_sample = min(decoded_samples, key=lambda x: x.energy)
    # 输出最优解
    best_sample.sample
    return best_sample.sample

G=random_graph(10)
H_t=binary_array(G)
result = solver(H_t)
pprint.pprint(result)
{'b[0][0]': 1,
 'b[1][0]': 1,
 'b[2][0]': 1,
 'b[3][0]': 0,
 'b[4][0]': 0,
 'b[5][0]': 0,
 'b[6][0]': 1,
 'b[7][0]': 1,
 'b[8][0]': 0,
 'b[9][0]': 0}