新手教程-使用真机-云平台或者SDK#

用例描述#

通过旅行商问题(TravelingSalesmanProblem，TSP)展示从建模、提交Qubo矩阵到云平台、从云平台获取计算结果的全流程

方式一：通过云平台上传QUBO矩阵计算#

代码建模并生成qubo矩阵#

 1# pylint: disable=<R0801>
 2"""
 3TSP problem solved using CIM.
 4"""
 5import numpy as np
 6import pandas as pd
 7import kaiwu as kw
 8import kaiwu.core._binary_expression
 9
10
11def is_edge_used(var_x, var_u, var_v):
12    """
13    Determine whether the edge (u, v) is used in the path.
14
15    Args:
16        var_x (ndarray): Decision variable matrix.
17
18        var_u (int): Start node.
19
20        var_v (int): End node.
21
22    Returns:
23        ndarray: Decision variable corresponding to the edge (u, v).
24    """
25    return kaiwu.core.quicksum([var_x[var_u, j] * var_x[var_v, j + 1] for j in range(-1, n - 1)])
26
27
28if __name__ == '__main__':
29    # Import distance matrix
30    w = np.array([[0, 1, 2],
31                  [1, 0, 0],
32                  [2, 0, 0]])
33    # Get the number of nodes
34    n = w.shape[0]
35
36    # Create qubo variable matrix
37    x = kaiwu.core.ndarray((n, n), "x", kaiwu.core.Binary)
38
39    # Get sets of edge and non-edge pairs
40    edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] != 0]
41    no_edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] == 0]
42
43    qubo_model = kw.qubo.QuboModel()
44    # TSP path cost
45    qubo_model.set_objective(
46        kaiwu.core.quicksum([w[u, v] * is_edge_used(x, u, v) for u, v in edges]))
47
48    # Node constraint: Each node must belong to exactly one position
49    qubo_model.add_constraint((x.sum(axis=0) - 1) ** 2 == 0, "sequence_cons", penalty=5.0)
50
51    # Position constraint: Each position can have only one node
52    qubo_model.add_constraint((x.sum(axis=1) - 1) ** 2 == 0, "node_cons", penalty=5.0)
53
54    # Edge constraint: Pairs without edges cannot appear in the path
55    qubo_model.add_constraint(kaiwu.core.quicksum([is_edge_used(x, u, v) for u, v in no_edges]) == 0,
56        "connect_cons", penalty=20)
57
58    qubo_mat = qubo_model.get_matrix()
59    pd.DataFrame(qubo_mat).to_csv("tsp.csv", index=False, header=False)

登录云平台上传矩阵#

  1. 登录光量子云计算平台后进入控制台，选择真机后点击新建任务

    ../../_images/new_task.png

  2. 进入任务配置页面后，填写任务名称、上传矩阵，确认后点击下一步

    ../../_images/new_task1.png

  3. 进入确认配置页面，确认任务和真机信息后点击确定按钮

    ../../_images/new_task2.png

  4. 进入提交任务页面，显示提交成功

    ../../_images/new_task3.png

  5. 返回控制台，任务正在校验中

    ../../_images/new_task4.jpg

  6. 校验成功后任务进入排队中状态

    ../../_images/new_task5.jpg

  7. 任务完成后点击详情进入结果详情页面

    ../../_images/new_task6.png

  8. 查看结果详情（qubo解向量、qubo value演化曲线、任务执行时间等）

    ../../_images/new_task7.png

方式二：直接使用SDK调用真机#

下面是同样一个TSP的问题，使用SDK直接调用真机求解的例子。 由于量子计算机有精度限制，例子中用SDK自带的PrecisionReducer进行精度适配。 想了解更多的关于精度的知识，

参见

 1"""
 2TSP calling CIM example
 3"""
 4import kaiwu as kw
 5import numpy as np
 6from kaiwu.common import CheckpointManager as ckpt
 7
 8# Define edges using conditional functions
 9def is_edge_used(var_x, var_u, var_v):
10    """
11    Determine whether the edge (u, v) is used in the path.
12
13    Args:
14        var_x (ndarray): Decision variable matrix.
15
16        var_u (int): Start node.
17
18        var_v (int): End node.
19
20    Returns:
21        ndarray: Decision variable corresponding to the edge (u, v).
22    """
23    return kw.core.quicksum([var_x[var_u, j] * var_x[var_v, j + 1] for j in range(-1, n - 1)])
24
25
26if __name__ == "__main__":
27    # Set the save path for intermediate files
28    kw.common.CheckpointManager.save_dir = '/tmp'
29    # Define distance matrix
30    w = np.array([[0, 0, 1, 1, 0],
31                  [0, 0, 1, 0, 1],
32                  [1, 1, 0, 0, 1],
33                  [1, 0, 0, 0, 1],
34                  [0, 1, 1, 1, 0]])
35
36    n = w.shape[0]  # Number of nodes
37
38    # Create a QUBO variable matrix (n x n)
39    x = kw.core.ndarray((n, n), "x", kw.core.Binary)
40
41    # Generate the set of edges and the set of non-edges
42    edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] != 0]
43    no_edges = [(u, v) for u in range(n) for v in range(n) if w[u, v] == 0]
44
45    # Initialize the QUBO model
46    qubo_model = kw.qubo.QuboModel()
47
48    # Set the objective function: minimize path cost
49    path_cost = kw.core.quicksum([w[u, v] * is_edge_used(x, u, v) for u, v in edges])
50    qubo_model.set_objective(path_cost)
51
52    # Add constraints
53    # Node constraints: Each node must occupy one position
54    qubo_model.add_constraint((x.sum(axis=0) - 1) ** 2 == 0, "node_cons", penalty=5.0)
55
56    # Location constraint: Each location must have at least one node.
57    qubo_model.add_constraint((x.sum(axis=1) - 1) ** 2 == 0, "pos_cons", penalty=5.0)
58
59    # Edge constraint: Non-connecting edges must not appear
60    qubo_model.add_constraint(
61        kw.core.quicksum([is_edge_used(x, u, v) for u, v in no_edges])==0,
62        "edge_cons", penalty=5
63    )
64
65    # Configure the solver
66    ckpt.save_dir = './tmp'
67    optimizer = kw.cim.CIMOptimizer(task_name_prefix="tsp")
68    optimizer = kw.cim.PrecisionReducer(optimizer, 8)
69    solver = kw.solver.SimpleSolver(optimizer)
70    sol_dict, qubo_val = solver.solve_qubo(qubo_model)
71
72    if sol_dict is not None:
73        # Verification Results
74        unsatisfied, res_dict = qubo_model.verify_constraint(sol_dict)
75        print(f"Number of unsatisfied constraints: {unsatisfied}")
76        print(f"constraint value: {res_dict}")
77
78        # Calculate path cost
79        path_cost = kw.core.get_val(qubo_model.objective, sol_dict)
80        print(f"Actual path cost: {path_cost}")
81    else:
82        print("Try again later")
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