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I had to try the example of qiskit’s Traveling Salesman Problem with 3 nodes and executing it at IBM backend called simulator_statevector.Can execute and get the result normally.

But when trying to solve the TSP problem with more than 3 nodes,I changed n = 3 to n = 4.

# Generating a graph of 3 nodes
n = 4
num_qubits = n ** 2
ins = tsp.random_tsp(n, seed=123)
print('distance\n', ins.w)

# Draw the graph
G = nx.Graph()
G.add_nodes_from(np.arange(0, ins.dim, 1))
colors = ['r' for node in G.nodes()]

for i in range(0, ins.dim):
    for j in range(i+1, ins.dim):
        G.add_edge(i, j, weight=ins.w[i,j])

pos = {k: v for k, v in enumerate(ins.coord)}

draw_graph(G, colors, pos)

And I changed backend from Aer.get_backend ('statevector_simulator') running on my device to provider.backend.simulator_statevector running on the IBM backend.

aqua_globals.random_seed = np.random.default_rng(123)
seed = 10598
backend = provider.backend.simulator_statevector
#backend = Aer.get_backend('statevector_simulator')

quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)

But the result that comes out with an error.

energy: -1303102.65625
time: 5626.549758911133
feasible: False
solution: [1, 0, 2, []]
solution objective: []
Traceback (most recent call last):
  File "<ipython-input-10-bc5619b5292f>", line 14, in <module>
    draw_tsp_solution(G, z, colors, pos)
  File "<ipython-input-4-999185567031>", line 29, in draw_tsp_solution
    G2.add_edge(order[i], order[j], weight=G[order[i]][order[j]]['weight'])
  File "/opt/conda/lib/python3.8/site-packages/networkx/classes/coreviews.py", line 51, in __getitem__
    return self._atlas[key]
TypeError: unhashable type: 'list'

Use %tb to get the full traceback.

How should I fix it? Please give me some advice.

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    $\begingroup$ Hello! This error comes from the fact that you don't reach a feasible solution, so the function isn't able to draw this non-feasible solution you have. In order to reach a feasible solution, may I suggest maybe playing with the shots of the simulator by increasing it so you may have a better result? Or increasing the size of the Ansatz, so you have more parameters and will be able to reach more potential solutions? But not too much, otherwise it will be too complex to solve. $\endgroup$ – Lena Apr 6 at 8:36

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