Row 4458

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Trying to implement Traveling Salesman problem solution using Quantum service via vscode and python, but stuck on this error. Full code given below:

import matplotlib.pyplot as plt import networkx as nx from qiskit.circuit.library import TwoLocal from qiskit_optimization.applications import Tsp from qiskit_algorithms.optimizers import SPSA from qiskit_algorithms.utils import algorithm_globals from qiskit_optimization.converters import QuadraticProgramToQubo from qiskit_ibm_runtime import SamplerV2 as Sampler from qiskit_ibm_runtime import QiskitRuntimeService def draw_graph(G, colors, pos): default_axes = plt.axes(frameon=True) nx.draw_networkx(G, node_color=colors, node_size=600, alpha=0.8, ax=default_axes, pos=pos) edge_labels = nx.get_edge_attributes(G, "weight") nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels) def draw_tsp_solution(G, order, colors, pos): G2 = nx.DiGraph() G2.add_nodes_from(G) n = len(order) for i in range(n): j = (i + 1) % n G2.add_edge(order[i], order[j], weight=G[order[i]][order[j]]["weight"]) default_axes = plt.axes(frameon=True) nx.draw_networkx( G2, node_color=colors, edge_color="b", node_size=600, alpha=0.8, ax=default_axes, pos=pos ) edge_labels = nx.get_edge_attributes(G2, "weight") nx.draw_networkx_edge_labels(G2, pos, font_color="b", edge_labels=edge_labels) # Generating a graph of 3 nodes n = 3 tsp = Tsp.create_random_instance(n, seed=123) adj_matrix = nx.to_numpy_array(tsp.graph) print("distance\n", adj_matrix) colors = ["r" for node in tsp.graph.nodes] pos = [tsp.graph.nodes[node]["pos"] for node in tsp.graph.nodes] draw_graph(tsp.graph, colors, pos) qp = tsp.to_quadratic_program() print(qp.prettyprint()) qp2qubo = QuadraticProgramToQubo() qubo = qp2qubo.convert(qp) qubitOp, offset = qubo.to_ising() print("Offset:", offset) print("Ising Hamiltonian:") print(str(qubitOp)) algorithm_globals.random_seed = 123 seed = 10598 optimizer = SPSA(maxiter=300) ry = TwoLocal(qubitOp.num_qubits, "ry", "cz", reps=5, entanglement="linear") # For an IBM Quantum account. ibm_quantum_service = QiskitRuntimeService(channel="ibm_quantum", token="xxxxx") service = QiskitRuntimeService() #Optimize problem for quantum execution. backend = service.least_busy(operational=True, simulator=False) # Define the QuantumCircuit from PauliSumOp qubit_circuit = qubitOp.to_circuit() sampler = Sampler(backend=backend) sampler.options.default_shots = 1024 # Options can be set using auto-complete. result = sampler.run(qubit_circuit) print("energy:", result.eigenvalue.real) print("time:", result.optimizer_time) x = tsp.sample_most_likely(result.eigenstate) z = tsp.interpret(x) print("solution:", z) print("solution objective:", tsp.tsp_value(z, adj_matrix)) draw_tsp_solution(tsp.graph, z, colors, pos) print(f"Job ID is {result.job_id()}")

The output I'm getting is given below.

(new_qiskit_env) PS F:\XXXXX> & f:/XXXX/new_qiskit_env/Scripts/python.exe f:/XXXXXXX/tsp_qc_ibm distance [[ 0. 48. 91.] [48. 0. 63.] [91. 63. 0.]] Problem name: TSP Minimize 48*x_0_0*x_1_1 + 48*x_0_0*x_1_2 + 91*x_0_0*x_2_1 + 91*x_0_0*x_2_2 + 48*x_0_1*x_1_0 + 48*x_0_1*x_1_2 + 91*x_0_1*x_2_0 + 91*x_0_1*x_2_2 + 48*x_0_2*x_1_0 + 48*x_0_2*x_1_1 + 91*x_0_2*x_2_0 + 91*x_0_2*x_2_1 + 63*x_1_0*x_2_1 + 63*x_1_0*x_2_2 + 63*x_1_1*x_2_0 + 63*x_1_1*x_2_2 + 63*x_1_2*x_2_0 + 63*x_1_2*x_2_1 Subject to Linear constraints (6) x_0_0 + x_0_1 + x_0_2 == 1 'c0' x_1_0 + x_1_1 + x_1_2 == 1 'c1' x_2_0 + x_2_1 + x_2_2 == 1 'c2' x_0_0 + x_1_0 + x_2_0 == 1 'c3' x_0_1 + x_1_1 + x_2_1 == 1 'c4' x_0_2 + x_1_2 + x_2_2 == 1 'c5' Binary variables (9) x_0_0 x_0_1 x_0_2 x_1_0 x_1_1 x_1_2 x_2_0 x_2_1 x_2_2 Offset: 7581.0 Ising Hamiltonian: SparsePauliOp(['IIIIIIIIZ', 'IIIIIIIZI', 'IIIIIIZII', 'IIIIIZIII', 'IIIIZIIII', 'IIIZIIIII', 'IIZIIIIII', 'IZIIIIIII', 'ZIIIIIIII', 'IIIIIIIZZ', 'IIIIIIZIZ', 'IIIIIZIIZ', 'IIIIZIIIZ', 'IIIZIIIIZ', 'IIZIIIIIZ', 'IZIIIIIIZ', 'ZIIIIIIIZ', 'IIIIIIZZI', 'IIIIIZIZI', 'IIIIZIIZI', 'IIIZIIIZI', 'IIZIIIIZI', 'IZIIIIIZI', 'ZIIIIIIZI', 'IIIIIZZII', 'IIIIZIZII', 'IIIZIIZII', 'IIZIIIZII', 'IZIIIIZII', 'ZIIIIIZII', 'IIIIZZIII', 'IIIZIZIII', 'IIZIIZIII', 'IZIIIZIII', 'ZIIIIZIII', 'IIIZZIIII', 'IIZIZIIII', 'IZIIZIIII', 'ZIIIZIIII', 'IIZZIIIII', 'IZIZIIIII', 'ZIIZIIIII', 'IZZIIIIII', 'ZIZIIIIII', 'ZZIIIIIII'], coeffs=[-1282.5 +0.j, -1282.5 +0.j, -1282.5 +0.j, -1268.5 +0.j, -1268.5 +0.j, -1268.5 +0.j, -1290. +0.j, -1290. +0.j, -1290. +0.j, 606.5 +0.j, 606.5 +0.j, 606.5 +0.j, 12. +0.j, 12. +0.j, 606.5 +0.j, 22.75+0.j, 22.75+0.j, 606.5 +0.j, 12. +0.j, 606.5 +0.j, 12. +0.j, 22.75+0.j, 606.5 +0.j, 22.75+0.j, 12. +0.j, 12. +0.j, 606.5 +0.j, 22.75+0.j, 22.75+0.j, 606.5 +0.j, 606.5 +0.j, 606.5 +0.j, 606.5 +0.j, 15.75+0.j, 15.75+0.j, 606.5 +0.j, 15.75+0.j, 606.5 +0.j, 15.75+0.j, 15.75+0.j, 15.75+0.j, 606.5 +0.j, 606.5 +0.j, 606.5 +0.j, 606.5 +0.j]) Traceback (most recent call last): File "f:\XXXXX\tsp_qc_ibm", line 77, in <module> qubit_circuit = qubitOp.to_circuit() ^^^^^^^^^^^^^^^^^^ AttributeError: 'SparsePauliOp' object has no attribute 'to_circuit'

If I dont use the to\_circuit() and try to pass the problem directly to the sampler, the following error occurs.

result = sampler.run(qubitOp)

Error.

raise TypeError("circuit must be QuantumCircuit.") TypeError: circuit must be QuantumCircuit.

FieldValue
text Trying to implement Traveling Salesman problem solution using Quantum service via vscode and python, but stuck on this error. Full code given below: import matplotlib.pyplot as plt import networkx as nx from qiskit.circuit.library import TwoLocal from qiskit_optimization.applications import Tsp from qiskit_algorithms.optimizers import SPSA from qiskit_algorithms.utils import algorithm_globals from qiskit_optimization.converters import QuadraticProgramToQubo …
label r/quantumcomputing
dataType post
communityName r/QuantumComputing
datetime 2024-04-12
username_encoded Z0FBQUFBQm5LakwxQVpITWhYai1LMDdlWHYzVHNtd2V0VEtsZVY3NndJN1YyS0FJMzVMYTB0b3dXSlBkVm5xNDRGeTRBczh5VDFYWHdhSDV2ei1KUkN5dG1LVm1HSmhwMVE9PQ==
url_encoded Z0FBQUFBQm5Lak9GY1dkYVJtUzBYV2x6WFV1YldMLWJvaDBBV1MtMWIyQklZZjRldjI2RXFEN2UyUS1XN1BFdE41OHNIcmtaZjFKSXVwLUJGa2JMRnBsWmJsMUJHa2x5TTM0RWhYRFQ2Z1lGQWR5MkdSUnZ4QnhMWC0tWGgxVHZGWG5fX2ozMXVkZkdwdl9nczJwd3dGTUVSNURMRnlUaWVQMTZnalBnYXprVGgwTnQ4Q09wZ2toR1FFZGlFSWQ0bDhrZktoX3hTY2RDVUd3TWhMMlpwTDI3MEwyMlpSbDgxUT09

Raw Record

{
  "text": "Trying to implement Traveling Salesman problem solution using Quantum service via vscode and python, but stuck on this error. Full code given below:\n\n    import matplotlib.pyplot as plt\n    import networkx as nx\n    \n    from qiskit.circuit.library import TwoLocal\n    from qiskit_optimization.applications import Tsp\n    from qiskit_algorithms.optimizers import SPSA\n    from qiskit_algorithms.utils import algorithm_globals\n    from qiskit_optimization.converters import QuadraticProgramToQubo\n    from qiskit_ibm_runtime import SamplerV2 as Sampler\n    \n    from qiskit_ibm_runtime import QiskitRuntimeService\n    \n    \n    def draw_graph(G, colors, pos):\n        default_axes = plt.axes(frameon=True)\n        nx.draw_networkx(G, node_color=colors, node_size=600, alpha=0.8, ax=default_axes, pos=pos)\n        edge_labels = nx.get_edge_attributes(G, \"weight\")\n        nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels)\n    \n    def draw_tsp_solution(G, order, colors, pos):\n        G2 = nx.DiGraph()\n        G2.add_nodes_from(G)\n        n = len(order)\n        for i in range(n):\n            j = (i + 1) % n\n            G2.add_edge(order[i], order[j], weight=G[order[i]][order[j]][\"weight\"])\n        default_axes = plt.axes(frameon=True)\n        nx.draw_networkx(\n            G2, node_color=colors, edge_color=\"b\", node_size=600, alpha=0.8, ax=default_axes, pos=pos\n        )\n        edge_labels = nx.get_edge_attributes(G2, \"weight\")\n        nx.draw_networkx_edge_labels(G2, pos, font_color=\"b\", edge_labels=edge_labels)\n    \n    # Generating a graph of 3 nodes\n    n = 3\n    tsp = Tsp.create_random_instance(n, seed=123)\n    adj_matrix = nx.to_numpy_array(tsp.graph)\n    print(\"distance\\n\", adj_matrix)\n    \n    colors = [\"r\" for node in tsp.graph.nodes]\n    pos = [tsp.graph.nodes[node][\"pos\"] for node in tsp.graph.nodes]\n    draw_graph(tsp.graph, colors, pos)\n    \n    qp = tsp.to_quadratic_program()\n    print(qp.prettyprint())\n    \n    qp2qubo = QuadraticProgramToQubo()\n    qubo = qp2qubo.convert(qp)\n    qubitOp, offset = qubo.to_ising()\n    print(\"Offset:\", offset)\n    print(\"Ising Hamiltonian:\")\n    print(str(qubitOp))\n    \n    algorithm_globals.random_seed = 123\n    seed = 10598\n    \n    optimizer = SPSA(maxiter=300)\n    ry = TwoLocal(qubitOp.num_qubits, \"ry\", \"cz\", reps=5, entanglement=\"linear\")\n    \n    \n    # For an IBM Quantum account.\n    ibm_quantum_service = QiskitRuntimeService(channel=\"ibm_quantum\", token=\"xxxxx\")\n    \n    service = QiskitRuntimeService()\n    \n    #Optimize problem for quantum execution.\n    backend = service.least_busy(operational=True, simulator=False)\n    \n    # Define the QuantumCircuit from PauliSumOp\n    qubit_circuit = qubitOp.to_circuit()\n    \n    sampler = Sampler(backend=backend)\n    sampler.options.default_shots = 1024  # Options can be set using auto-complete.\n    \n    result = sampler.run(qubit_circuit)\n    \n    print(\"energy:\", result.eigenvalue.real)\n    print(\"time:\", result.optimizer_time)\n    x = tsp.sample_most_likely(result.eigenstate)\n    z = tsp.interpret(x)\n    print(\"solution:\", z)\n    print(\"solution objective:\", tsp.tsp_value(z, adj_matrix))\n    draw_tsp_solution(tsp.graph, z, colors, pos)\n    \n    print(f\"Job ID is {result.job_id()}\")\n\nThe output I'm getting is given below.\n\n    (new_qiskit_env) PS F:\\XXXXX> & f:/XXXX/new_qiskit_env/Scripts/python.exe f:/XXXXXXX/tsp_qc_ibm\n    distance\n     [[ 0. 48. 91.]\n     [48.  0. 63.]\n     [91. 63.  0.]]\n    Problem name: TSP\n    \n    Minimize\n      48*x_0_0*x_1_1 + 48*x_0_0*x_1_2 + 91*x_0_0*x_2_1 + 91*x_0_0*x_2_2\n      + 48*x_0_1*x_1_0 + 48*x_0_1*x_1_2 + 91*x_0_1*x_2_0 + 91*x_0_1*x_2_2\n      + 48*x_0_2*x_1_0 + 48*x_0_2*x_1_1 + 91*x_0_2*x_2_0 + 91*x_0_2*x_2_1\n      + 63*x_1_0*x_2_1 + 63*x_1_0*x_2_2 + 63*x_1_1*x_2_0 + 63*x_1_1*x_2_2\n      + 63*x_1_2*x_2_0 + 63*x_1_2*x_2_1\n    \n    Subject to\n      Linear constraints (6)\n        x_0_0 + x_0_1 + x_0_2 == 1  'c0'\n        x_1_0 + x_1_1 + x_1_2 == 1  'c1'\n        x_2_0 + x_2_1 + x_2_2 == 1  'c2'\n        x_0_0 + x_1_0 + x_2_0 == 1  'c3'\n        x_0_1 + x_1_1 + x_2_1 == 1  'c4'\n        x_0_2 + x_1_2 + x_2_2 == 1  'c5'\n    \n      Binary variables (9)\n        x_0_0 x_0_1 x_0_2 x_1_0 x_1_1 x_1_2 x_2_0 x_2_1 x_2_2\n    \n    Offset: 7581.0\n    Ising Hamiltonian:\n    SparsePauliOp(['IIIIIIIIZ', 'IIIIIIIZI', 'IIIIIIZII', 'IIIIIZIII', 'IIIIZIIII', 'IIIZIIIII', 'IIZIIIIII', 'IZIIIIIII', 'ZIIIIIIII', 'IIIIIIIZZ', 'IIIIIIZIZ', 'IIIIIZIIZ', 'IIIIZIIIZ', 'IIIZIIIIZ', 'IIZIIIIIZ', 'IZIIIIIIZ', 'ZIIIIIIIZ', 'IIIIIIZZI', 'IIIIIZIZI', 'IIIIZIIZI', 'IIIZIIIZI', 'IIZIIIIZI', 'IZIIIIIZI', 'ZIIIIIIZI', 'IIIIIZZII', 'IIIIZIZII', 'IIIZIIZII', 'IIZIIIZII', 'IZIIIIZII', 'ZIIIIIZII', 'IIIIZZIII', 'IIIZIZIII', 'IIZIIZIII', 'IZIIIZIII', 'ZIIIIZIII', 'IIIZZIIII', 'IIZIZIIII', 'IZIIZIIII', 'ZIIIZIIII', 'IIZZIIIII', 'IZIZIIIII', 'ZIIZIIIII', 'IZZIIIIII', 'ZIZIIIIII', 'ZZIIIIIII'],      \n                  coeffs=[-1282.5 +0.j, -1282.5 +0.j, -1282.5 +0.j, -1268.5 +0.j, -1268.5 +0.j,\n     -1268.5 +0.j, -1290.  +0.j, -1290.  +0.j, -1290.  +0.j,   606.5 +0.j,\n       606.5 +0.j,   606.5 +0.j,    12.  +0.j,    12.  +0.j,   606.5 +0.j,\n        22.75+0.j,    22.75+0.j,   606.5 +0.j,    12.  +0.j,   606.5 +0.j,\n        12.  +0.j,    22.75+0.j,   606.5 +0.j,    22.75+0.j,    12.  +0.j,\n        12.  +0.j,   606.5 +0.j,    22.75+0.j,    22.75+0.j,   606.5 +0.j,\n       606.5 +0.j,   606.5 +0.j,   606.5 +0.j,    15.75+0.j,    15.75+0.j,\n       606.5 +0.j,    15.75+0.j,   606.5 +0.j,    15.75+0.j,    15.75+0.j,\n        15.75+0.j,   606.5 +0.j,   606.5 +0.j,   606.5 +0.j,   606.5 +0.j])\n    Traceback (most recent call last):\n      File \"f:\\XXXXX\\tsp_qc_ibm\", line 77, in <module>\n        qubit_circuit = qubitOp.to_circuit()\n                        ^^^^^^^^^^^^^^^^^^\n    AttributeError: 'SparsePauliOp' object has no attribute 'to_circuit'\n\nIf I dont use the to\\_circuit() and try to pass the problem directly to the sampler, the following error occurs.\n\n    result = sampler.run(qubitOp)\n\nError.\n\n        raise TypeError(\"circuit must be QuantumCircuit.\")\n    TypeError: circuit must be QuantumCircuit.",
  "label": "r/quantumcomputing",
  "dataType": "post",
  "communityName": "r/QuantumComputing",
  "datetime": "2024-04-12",
  "username_encoded": "Z0FBQUFBQm5LakwxQVpITWhYai1LMDdlWHYzVHNtd2V0VEtsZVY3NndJN1YyS0FJMzVMYTB0b3dXSlBkVm5xNDRGeTRBczh5VDFYWHdhSDV2ei1KUkN5dG1LVm1HSmhwMVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GY1dkYVJtUzBYV2x6WFV1YldMLWJvaDBBV1MtMWIyQklZZjRldjI2RXFEN2UyUS1XN1BFdE41OHNIcmtaZjFKSXVwLUJGa2JMRnBsWmJsMUJHa2x5TTM0RWhYRFQ2Z1lGQWR5MkdSUnZ4QnhMWC0tWGgxVHZGWG5fX2ozMXVkZkdwdl9nczJwd3dGTUVSNURMRnlUaWVQMTZnalBnYXprVGgwTnQ4Q09wZ2toR1FFZGlFSWQ0bDhrZktoX3hTY2RDVUd3TWhMMlpwTDI3MEwyMlpSbDgxUT09"
}
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