Initialization

Initialization#

from juniqutils import save_ising_file, load_ising_file
import json
import dimod
import dwave
from dwave.system import DWaveSampler,EmbeddingComposite
dwave.system.__version__, dimod.__version__

('1.32.0', '0.12.20')

Verification#

TSP_DATA = json.load(open('tsp_data.json'))
CITIES = TSP_DATA['CITIES']

Exact solver#

problem = load_ising_file('../problems/001.ising')
bqm = dimod.BQM.from_ising(problem['hs'], problem['Js'], problem['OFFSET'])

result = dimod.ExactSolver().sample(bqm).to_pandas_dataframe().sort_values('energy').reset_index(drop=True)
result
x0,0 x0,1 x0,2 x0,3 x1,0 x1,1 x1,2 x1,3 x2,0 x2,1 x2,2 x2,3 x3,0 x3,1 x3,2 x3,3 energy num_occurrences
0 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 0.5922 1
1 -1 -1 1 -1 1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 0.5922 1
2 -1 1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 1 -1 0.5922 1
3 -1 1 -1 -1 -1 -1 -1 1 -1 -1 1 -1 1 -1 -1 -1 0.5922 1
4 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 1 -1 1 -1 -1 0.5922 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
65531 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 68.9884 1
65532 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 68.9884 1
65533 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 68.9884 1
65534 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 68.9884 1
65535 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 79.7472 1

65536 rows × 18 columns

n = problem['METADATA']['n']
tourmatrix = result.loc[0][:n**2].to_numpy().reshape((n,n))
tourmatrix

array([[-1., -1.,  1., -1.],
       [ 1., -1., -1., -1.],
       [-1., -1., -1.,  1.],
       [-1.,  1., -1., -1.]])

for t in range(n):
    for i in range(n):
        if tourmatrix[i,t] == 1:
            print(f'At time {t}, we are in city {i} ({CITIES[i]})')

At time 0, we are in city 1 (Madrid)
At time 1, we are in city 3 (Paris)
At time 2, we are in city 0 (Berlin)
At time 3, we are in city 2 (Rome)

print(result.loc[0].energy / problem['METADATA']['scale'])
print(problem['METADATA']['optimal_cost'])

59.21999999999896
59.22

QPU#

sampler = DWaveSampler(profile='defaults')
solver = EmbeddingComposite(sampler)
print(f'Selected QPU {sampler.properties["chip_id"]} with {len(sampler.nodelist)} qubits')

Selected QPU Advantage_system4.1 with 5627 qubits

problem = load_ising_file('../problems/002.ising')
bqm = dimod.BQM.from_ising(problem['hs'], problem['Js'], problem['OFFSET'])
result = solver.sample(bqm,num_reads=1000).to_pandas_dataframe()
result
x0,0 x0,1 x0,2 x0,3 x0,4 x1,0 x1,1 x1,2 x1,3 x1,4 ... x3,3 x3,4 x4,0 x4,1 x4,2 x4,3 x4,4 chain_break_fraction energy num_occurrences
0 -1 -1 -1 -1 1 -1 1 -1 -1 -1 ... -1 -1 -1 -1 -1 1 -1 0.0 0.6232 3
1 -1 1 -1 -1 -1 -1 -1 -1 1 -1 ... -1 -1 1 -1 -1 -1 -1 0.0 0.6232 17
2 -1 1 -1 -1 -1 -1 -1 -1 -1 1 ... -1 -1 -1 -1 1 -1 -1 0.0 0.6232 9
3 -1 -1 -1 -1 1 -1 -1 1 -1 -1 ... 1 -1 1 -1 -1 -1 -1 0.0 0.6232 14
4 -1 -1 1 -1 -1 -1 -1 -1 -1 1 ... 1 -1 -1 1 -1 -1 -1 0.0 0.6232 8
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
396 1 -1 -1 -1 -1 -1 -1 -1 -1 1 ... 1 -1 -1 1 -1 -1 1 0.0 5.2155 1
397 1 -1 -1 -1 -1 -1 -1 -1 -1 1 ... 1 -1 1 -1 -1 -1 1 0.0 5.2164 1
398 -1 -1 -1 1 -1 -1 -1 -1 1 -1 ... -1 -1 1 -1 1 -1 -1 0.0 5.2710 1
399 1 -1 -1 -1 -1 -1 -1 -1 1 1 ... -1 -1 -1 1 1 -1 -1 0.0 5.3011 1
400 -1 -1 -1 -1 1 1 -1 1 -1 -1 ... -1 -1 -1 1 -1 1 -1 0.0 5.6011 1

401 rows × 28 columns

# the states with energy 0.6232 are the ground sate, corresponding to the optimal cost
problem['METADATA']['optimal_cost']

62.32
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