def test_sa(self):
for i in range(self.trotterslices):
spinVector = np.array([ 2*self.rng.randint(2)-1
for k in range(self.nspins) ],
dtype=np.float64)
tannealingsched = np.linspace(self.preannealingtemp,
self.annealingtemp,
self.annealingsteps)
sa.Anneal(tannealingsched,
self.annealingmcsteps,
spinVector,
self.nbs,
self.rng)
# with the given parameters, they have surely found a ground state
assert(np.sum(spinVector[:4]) == 4 or
np.sum(spinVector) == -8)
assert(sa.ClassicalIsingEnergy(spinVector, self.isingJ) == -8.0)
# nspins,
# isingJ,
# 4,
# 'ising_instances/santoro_80x80_neighbors.npy'
# )
neighbors = np.load('ising_instances/santoro_80x80_neighbors.npy')
# initialize the states
spinVector = np.array([2 * rng.randint(2) - 1 for k in range(nspins)],
dtype=np.float)
configurations = np.tile(spinVector, (trotterslices, 1)).T
print("Initial state energy: ", sa.ClassicalIsingEnergy(spinVector, isingJ))
print '\n'
# Try just using SA
tannealingsched = np.linspace(preannealingtemp, annealingtemp, annealingsteps)
t0 = time.time()
sa.Anneal(tannealingsched, annealingmcsteps, spinVector, neighbors, rng)
t1 = time.time()
print("Final SA energy: ", sa.ClassicalIsingEnergy(spinVector, isingJ))
print str(
np.sum(spinVector == groundstate)) + '/' + str(nspins) + ' spins agree'
print "SA time (seconds): ", str(t1 - t0)
print '\n'
# Now do PIQA
preannealingsched = np.linspace(preannealingtemp, annealingtemp, 100)
annealingsched = np.linspace(fieldstart, fieldend, annealingsteps)
t0 = time.time()
sa.Anneal(preannealingsched, 1, spinVector, neighbors, rng) # preannealing
qmc.QuantumAnneal(annealingsched, annealingmcsteps, trotterslices,
annealingtemp, nspins, configurations, neighbors, rng)
t1 = time.time()
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 8)
# keep a count of the populations
coinc_sa = np.zeros(2**nspins)
coinc_qa = np.zeros(2**nspins)
print("")
print("# samples", samples)
# Try using SA (random start)
for sa_itr in xrange(samples):
spinVector = np.array([2 * rng.randint(2) - 1 for k in range(nspins)],
dtype=np.float)
starten, startstate = (sa.ClassicalIsingEnergy(spinVector, isingJ),
getbitstr(spinVector))
sa.Anneal(tannealingsched, tannealingmcsteps, spinVector, neighbors, rng)
bitstr = getbitstr(spinVector)
coinc_sa[int(bitstr, 2)] += 1
# Now do PIQA
qmc_errors_diff = 0
for s in xrange(samples):
confs = np.tile(
np.array([2 * rng.randint(2) - 1 for k in range(nspins)],
dtype=np.float), (trotterslices, 1)).T
qmc.QuantumAnneal(annealingsched, annealingmcsteps, trotterslices,
annealingtemp, nspins, confs, neighbors, rng)
if not np.all(np.sum(confs, axis=1) / trotterslices == confs[:, 0]):
qmc_errors_diff += 1
else:
bitstr = reduce(lambda x, y: x + y,
])
# get the spin version
svec = 2 * msvec - 1
print("Initial state energy: ",
sorted([sa.ClassicalIsingEnergy(v, isingJ) for v in svec]))
print '\n'
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 4)
# SA annealing schedule
tannealingsched = np.linspace(preannealingtemp, annealingtemp, annealingsteps)
# Normal annealing routine
t0 = time.time()
for vec in svec:
sa.Anneal(tannealingsched, annealingmcsteps, vec, neighbors, rng)
t1 = time.time()
print("Final SA energy: ",
sorted([sa.ClassicalIsingEnergy(v, isingJ) for v in svec]))
print "SA time (seconds): ", str(t1 - t0)
print '\n'
# Now try multispin encoding
t0 = time.time()
sa.Anneal_multispin(tannealingsched, annealingmcsteps, msvec, neighbors, rng)
t1 = time.time()
print(
"Final SA-multispin energies: ",
sorted(
[sa.ClassicalIsingEnergy(tools.bits2spins(v), isingJ) for v in msvec]))
print "SA-multispin time (seconds): ", str(t1 - t0)
