WORK IN PROGRESS: QAOA / RQAOA dumping data
This notebook shows how to use the dumping methods. Also, how to use the set_exp_tags and set_header methods.
[1]:
import networkx as nx
import numpy as np
from openqaoa.problems import MinimumVertexCover, NumberPartition, Knapsack
import matplotlib.pyplot as plt
from pprint import pprint
from openqaoa.backends import create_device, DeviceLocal
from openqaoa import QAOA
Problem Statement
[2]:
kp_prob = Knapsack.random_instance(n_items=5, quantum=True,seed=1234)
kp_qubo = kp_prob.qubo
QAOA header
[3]:
# create a qaoa object
q = QAOA()
[4]:
# here we show what we have after initialization in the header (we can see that everything is empty but the 'experiment_id' and 'algorithm' fields)
q.header
[4]:
{'atomic_id': None,
'experiment_id': '907f60a2-5b6a-42b8-831b-4189988b3dc9',
'project_id': None,
'algorithm': 'qaoa',
'description': None,
'run_by': None,
'provider': None,
'target': None,
'cloud': None,
'client': None,
'qubit_number': None,
'execution_time_start': None,
'execution_time_end': None}
[5]:
# set the header
q.set_header(
project_id="8353185c-b175-4eda-9628-b4e58cb0e41b",
description="test",
run_by="raul",
provider="-",
target="-",
cloud="local",
client="-",
)
[6]:
# compile
q.compile(kp_qubo)
[7]:
# here we show what we have in the header after setting it and the compilation
# in the compilation the 'atomic_id' is set, if we recompile the 'atomic_id' will change
# also the 'qubit_number' is set, taking the number of qubits from the qubo
q.header
[7]:
{'atomic_id': '3022f14a-4900-47f5-9516-1d214003800a',
'experiment_id': '907f60a2-5b6a-42b8-831b-4189988b3dc9',
'project_id': '8353185c-b175-4eda-9628-b4e58cb0e41b',
'algorithm': 'qaoa',
'description': 'test',
'run_by': 'raul',
'provider': '-',
'target': '-',
'cloud': 'local',
'client': '-',
'qubit_number': 9,
'execution_time_start': None,
'execution_time_end': None}
[8]:
# optimize problem
q.optimize()
[9]:
# here we show what we have in the header after optimization, now everything is set
q.header
[9]:
{'atomic_id': '3022f14a-4900-47f5-9516-1d214003800a',
'experiment_id': '907f60a2-5b6a-42b8-831b-4189988b3dc9',
'project_id': '8353185c-b175-4eda-9628-b4e58cb0e41b',
'algorithm': 'qaoa',
'description': 'test',
'run_by': 'raul',
'provider': '-',
'target': '-',
'cloud': 'local',
'client': '-',
'qubit_number': 9,
'execution_time_start': '2023-02-03T03:49:22',
'execution_time_end': '2023-02-03T03:49:22'}
QAOA as a dictionary
[10]:
q = QAOA()
qiskit_device = create_device(location='local', name='vectorized')
q.set_device(qiskit_device)
q.set_circuit_properties(p=1, param_type='standard', init_type='rand', mixer_hamiltonian='x')
q.set_backend_properties(prepend_state=None, append_state=None)
q.set_classical_optimizer(maxiter=15, method='vgd', jac="finite_difference")
# set the header
q.set_header(
project_id="8353185c-b175-4eda-9628-b4e58cb0e41b",
experiment_id="8353185c-b175-4eda-9628-b4e58cb0e400",
description="test",
run_by="raul",
provider="-",
target="-",
cloud="local",
client="-",
)
# set experimental tags
q.set_exp_tags(tags={
"tag1": "value1",
"tag2": "value2"
})
[11]:
q.compile(kp_qubo)
[12]:
q.optimize()
[13]:
qaoa_asdict = q.asdict()
print(qaoa_asdict.keys())
dict_keys(['header', 'data'])
[14]:
qaoa_asdict['header']
[14]:
{'atomic_id': 'c6e4df16-c0bc-43ea-b273-3d52fd73f15e',
'experiment_id': '8353185c-b175-4eda-9628-b4e58cb0e400',
'project_id': '8353185c-b175-4eda-9628-b4e58cb0e41b',
'algorithm': 'qaoa',
'description': 'test',
'run_by': 'raul',
'provider': '-',
'target': '-',
'cloud': 'local',
'client': '-',
'qubit_number': 9,
'execution_time_start': '2023-02-03T03:49:23',
'execution_time_end': '2023-02-03T03:49:23',
'metadata': {'tag1': 'value1',
'tag2': 'value2',
'problem_type': 'knapsack',
'n_shots': 100,
'optimizer_method': 'vgd',
'jac': 'finite_difference',
'param_type': 'standard',
'init_type': 'rand',
'p': 1}}
[15]:
qaoa_asdict['data']
[15]:
{'exp_tags': {'tag1': 'value1', 'tag2': 'value2'},
'input_problem': {'terms': [[0, 1],
[0, 2],
[0, 3],
[1, 2],
[1, 3],
[2, 3],
[4, 5],
[4, 6],
[4, 7],
[8, 4],
[5, 6],
[5, 7],
[8, 5],
[6, 7],
[8, 6],
[8, 7],
[0, 4],
[0, 5],
[0, 6],
[0, 7],
[0, 8],
[1, 4],
[1, 5],
[1, 6],
[1, 7],
[8, 1],
[2, 4],
[2, 5],
[2, 6],
[2, 7],
[8, 2],
[3, 4],
[3, 5],
[3, 6],
[3, 7],
[8, 3],
[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8]],
'weights': [8.0,
16.0,
32.0,
32.0,
64.0,
128.0,
16.0,
32.0,
16.0,
32.0,
8.0,
4.0,
8.0,
8.0,
16.0,
8.0,
16.0,
4.0,
8.0,
4.0,
8.0,
32.0,
8.0,
16.0,
8.0,
16.0,
64.0,
16.0,
32.0,
16.0,
32.0,
128.0,
32.0,
64.0,
32.0,
64.0,
4.0,
8.0,
16.0,
32.0,
18.0,
6.0,
10.0,
5.0,
8.5],
'constant': 216.5,
'n': 9,
'problem_instance': {'problem_type': 'knapsack',
'values': [4, 4, 4, 2, 1],
'weights': [4, 1, 2, 1, 2],
'weight_capacity': 13,
'penalty': 8,
'n_items': 5},
'metadata': {}},
'input_parameters': {'device': {'device_location': 'local',
'device_name': 'vectorized'},
'backend_properties': {'init_hadamard': True,
'n_shots': 100,
'prepend_state': None,
'append_state': None,
'cvar_alpha': 1,
'noise_model': None,
'qubit_layout': None,
'seed_simulator': None,
'qiskit_simulation_method': None,
'active_reset': None,
'rewiring': None,
'disable_qubit_rewiring': None},
'classical_optimizer': {'optimize': True,
'method': 'vgd',
'maxiter': 15,
'maxfev': None,
'jac': 'finite_difference',
'hess': None,
'constraints': None,
'bounds': None,
'tol': None,
'optimizer_options': None,
'jac_options': None,
'hess_options': None,
'parameter_log': True,
'optimization_progress': False,
'cost_progress': True,
'save_intermediate': False},
'circuit_properties': {'param_type': 'standard',
'init_type': 'rand',
'qubit_register': [],
'p': 1,
'q': None,
'variational_params_dict': {'q': None, 'total_annealing_time': 0.7},
'annealing_time': 0.7,
'linear_ramp_time': 0.7,
'mixer_hamiltonian': 'x',
'mixer_qubit_connectivity': None,
'mixer_coeffs': None,
'seed': None}},
'results': {'method': 'vgd',
'evals': {'number_of_evals': 77, 'jac_evals': 60, 'qfim_evals': 0},
'most_probable_states': {'solutions_bitstrings': ['010100100'],
'bitstring_energy': 4.0},
'intermediate': {'angles': [[1.696394537853, 2.595712606584],
[1.696394537853, 2.595712606584],
[1.996119067834, -50.328769100425],
[1.64899721785, -385.694963130427],
[2.059630894828, -387.383627862428],
[2.718757856834, -353.098122162415],
[3.166788834826, -381.087060361417],
[3.182734816826, -381.245561721422],
[3.230430103835, -382.492231644428],
[0.774184417829, -445.031778234416],
[1.78247387985, -522.41038271442],
[2.477285593877, -511.188344804403],
[2.124756497872, -532.051847827383],
[2.324583746892, -612.776430421385],
[2.4102619469, -348.047678058386],
[2.325951441898, -281.10976680538],
[2.264961223895, 189.551090999621]],
'cost': [215.036622653159,
215.036622653159,
227.373907240102,
214.856475631767,
196.698283560032,
229.019841661881,
216.530677718379,
216.463567563053,
240.337346150857,
216.831565289346,
208.609841808658,
180.347792284209,
209.704850720122,
167.15172430448,
192.608394059029,
228.477166999451,
191.279742660462],
'measurement_outcomes': [],
'job_id': ['7e268f06-6259-4665-b73d-77aa9bc52f48',
'fdd97f20-11f4-4207-b2dd-f426e06cbccb',
'278edfa5-05c6-459c-b90e-3287574c81c7',
'e40b57a0-31f9-4c7c-bd79-7d49dff06614',
'4d3193fe-58cc-4943-8a5c-fad7fc679ae3',
'9f6a24b4-26a6-4c98-9973-df88064dc8ef',
'4d00c5b2-3afa-4436-97bb-94eac763d4a8',
'076a3f72-5612-4749-b7f5-4c03fbfb731c',
'7bd1f184-a1a9-4f95-9144-14db36c60cd7',
'a18784bb-cf17-4286-a19a-97c58b86b760',
'eff50f77-0224-409f-83c0-504219e4d580',
'b0c20a57-28b8-4435-bc1b-a3b684f5f187',
'aa3ca3ae-9046-4721-98eb-09e29d29e9ae',
'8646cd83-af5e-4a38-b49e-0a5b6f025963',
'38c159dc-6910-4b8e-83b5-d277eaf40c86',
'7423e07b-d4f1-47ea-a9ee-505622e59fcd',
'5dbce945-3340-4fba-9048-f9edcaffc46e']},
'optimized': {'angles': [2.324583746892, -612.776430421385],
'cost': 167.15172430448,
'measurement_outcomes': array([ 1.47465215e-02-2.00730828e-02j, -7.27171252e-03-3.81774001e-03j,
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'job_id': '8646cd83-af5e-4a38-b49e-0a5b6f025963',
'eval_number': 14}}}
[ ]: