代码：

正确配置了user_id和sdk_code

# Import distance matrix
w = np.array([[0, 13, 11, 16, 8],
              [13, 0, 7, 14, 9],
              [11, 7, 0, 10, 9],
              [16, 14, 10, 0, 12],
              [8, 9, 9, 12, 0]])

# Get the number of nodes
n = w.shape[0]

# Create qubo variable matrix
x = kw.qubo.ndarray((n, n), "x", kw.qubo.Binary)

# Get sets of edge and non-edge pairs
edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] != 0]
no_edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] == 0]
# Node constraint: Each node must belong to exactly one position
sequence_cons = kw.qubo.quicksum([(1 - kw.qubo.quicksum([x[v, j] for j in range(n)])) ** 2 for v in range(n)])

# Position constraint: Each position can have only one node
node_cons = kw.qubo.quicksum([(1 - kw.qubo.quicksum([x[v, j] for v in range(n)])) ** 2 for j in range(n)])

# Edge constraint: Pairs without edges cannot appear in the path
connect_cons = kw.qubo.quicksum(
    [kw.qubo.quicksum([x[u, j] * x[v, j + 1] for j in range(n - 1)]) + x[u, n - 1] * x[v, 0] for u, v in no_edges])

# Hamiltonian cycle constraint: Sum of the above three constraints
ham_cycle = sequence_cons + node_cons + connect_cons

# TSP path cost
path_cost = kw.qubo.quicksum(
    [w[u, v] * (kw.qubo.quicksum([x[u, j] * x[v, j + 1] for j in range(n - 1)]) + x[u, n - 1] * x[v, 0]) for u, v in
     edges])

# Final objective function with penalty factor 100 for the Hamiltonian constraint
obj = 100 * ham_cycle + path_cost
# Parse QUBO
obj = kw.qubo.make(obj)

# Convert to Ising model
obj_ising = kw.qubo.qubo_model_to_ising_model(obj)

# Extract the Ising matrix
matrix = obj_ising.get_ising()["ising"]

# Perform calculation using CIM simulator
worker = kw.cim.SimulatedCIMOptimizer(
    pump=1.3,
    noise=0.2,
    laps=5000,
    delta_time=0.05,
    normalization=0.3,
    iterations=50
)
output = worker.solve(matrix)

# Sort the results
opt = kw.sampler.optimal_sampler(matrix, output, bias=0, negtail_ff=False)

# Select the best solution
cim_best = opt[0][0]

# If the linear term variable is -1, perform a flip
cim_best = cim_best * cim_best[-1]

print(cim_best)

# Get the list of variable names
vars = obj_ising.get_variables()

# Substitute the spin vector and obtain the result dictionary
sol_dict = kw.qubo.get_sol_dict(cim_best, vars)

# Check the hard constraints for validity and path length
seq_val = kw.qubo.get_val(sequence_cons, sol_dict)
node_val = kw.qubo.get_val(node_cons, sol_dict)
ham_val = kw.qubo.get_val(ham_cycle, sol_dict)
print('position cons: {}'.format(seq_val))
print('node_cons cons: {}'.format(node_val))
print('ham_cycle: {}'.format(ham_val))

# Calculate the path length using path_cost
path_val = kw.qubo.get_val(path_cost, sol_dict)
print('path_cost: {}'.format(path_val))
报错：

18810401804 2025-04-22 17:52:46

同学，kw.cim.SimulatedCIMOptimizer这个函数官方文档已经没有了，这个实例同学是在哪看到的呢？