**prefs):
return DenseGraphBFSearcher(W, optimize, dtype, prefs)
if __name__ == '__main__':
np.random.seed(0)
dtype = np.float32
N = 8
W = np.array([[-32, 4, 4, 4, 4, 4, 4, 4], [4, -32, 4, 4, 4, 4, 4, 4],
[4, 4, -32, 4, 4, 4, 4, 4], [4, 4, 4, -32, 4, 4, 4, 4],
[4, 4, 4, 4, -32, 4, 4, 4], [4, 4, 4, 4, 4, -32, 4, 4],
[4, 4, 4, 4, 4, 4, -32, 4], [4, 4, 4, 4, 4, 4, 4, -32]])
N = 16
W = sqaod.generate_random_symmetric_W(N, -0.5, 0.5, dtype)
bf = dense_graph_bf_searcher(W, sqaod.minimize, dtype)
bf.set_preferences(tile_size=256)
import time
start = time.time()
bf.search()
end = time.time()
print(end - start)
x = bf.get_x()
E = bf.get_E()
print(E)
print(x)
print(bf.get_preferences())
def dense_graph_random(N, dtype):
return sq.generate_random_symmetric_W(N, dtype=dtype)
if __name__ == '__main__':
import sqaod
import sqaod.py.formulas as py_formulas
dtype = np.float64
np.random.seed(0)
try:
W = np.ones((4, 4), np.int32)
dense_graph_calculate_hamiltonian(W, np.int32)
except Exception as e:
print(e.message)
# dense graph
N = 16
W = sqaod.generate_random_symmetric_W(N)
x = sqaod.generate_random_bits(N)
E0 = py_formulas.dense_graph_calculate_E(W, x)
E1 = dense_graph_calculate_E(W, x, dtype)
assert np.allclose(E0, E1)
xlist = sqaod.create_bitset_sequence(range(0, 1 << N), N)
E0 = py_formulas.dense_graph_batch_calculate_E(W, xlist)
E1 = dense_graph_batch_calculate_E(W, xlist, dtype)
assert np.allclose(E0, E1)
h0, J0, c0 = py_formulas.dense_graph_calculate_hamiltonian(W)
h1, J1, c1 = dense_graph_calculate_hamiltonian(W, dtype)
assert np.allclose(h0, h1)
assert np.allclose(J0, J1)
elapsedTimePerIter = warmup / nIters
nIters = int(duration / warmup * nIters) + 1
print('Done.')
print('Averarge time : {0} sec, Expected # iterations in {1} sec. : {2}'
.format(elapsedTimePerIter, duration, nIters))
sys.stdout.flush()
begin = timer();
for i in range(nIters) :
# print('.', end='', file=sys.stderr)
searcher.search();
searcher.make_solution()
end = timer()
elapsedTime = end - begin
elapsedTimePerIter = elapsedTime / nIters
print('Done,')
print('Averarge time : {0} sec, {1} iterations executed in {2} sec.'
.format(elapsedTimePerIter, nIters, elapsedTime))
sys.stdout.flush()
return nIters, elapsedTimePerIter
if __name__ == '__main__' :
N = 2048
W = sq.generate_random_symmetric_W(N, dtype=dtype)
W = dense_graph_random(N, np.float32)
an = sq.cpu.dense_graph_annealer(W, dtype=np.float32)
an.set_preferences(n_trotters = N)
elapsed, nIters = benchmark(an)
print(elapsed, nIters)
def dense_graph_random(N, dtype):
W = sqaod.generate_random_symmetric_W(N, dtype=dtype)
return _quantize(W, dtype)
W = np.array([[-32,4,4,4,4,4,4,4],
[4,-32,4,4,4,4,4,4],
[4,4,-32,4,4,4,4,4],
[4,4,4,-32,4,4,4,4],
[4,4,4,4,-32,4,4,4],
[4,4,4,4,4,-32,4,4],
[4,4,4,4,4,4,-32,4],
[4,4,4,4,4,4,4,-32]])
np.random.seed(0)
N = 8
m = 4
N = 100
m = 5
W = sqaod.generate_random_symmetric_W(N, -0.5, 0.5, np.float64)
ann = dense_graph_annealer(W, dtype=np.float64, n_trotters = m)
import sqaod.py as py
#ann = py.dense_graph_annealer(W, n_trotters = m)
ann.set_qubo(W, sqaod.minimize)
h, J, c = ann.get_hamiltonian()
print(h)
print(J)
print(c)
Ginit = 5.
Gfin = 0.001
nRepeat = 2
