def __init__(self,
num_reads=None,
initial_states_generator='random',
**runopts):
super(SteepestDescentSubproblemSampler, self).__init__(**runopts)
self.num_reads = num_reads
self.initial_states_generator = initial_states_generator
self.sampler = SteepestDescentSolver()
def main(nqubits, instance, T, chainstrength, numruns, greedy, inspect):
"""
Args:
nqubits (int): number of qubits for the file that contains the
information of an Exact Cover instance.
instance (int): intance used for the desired number of qubits.
T (float):
Returns:
"""
control, solution, clauses = functions.read_file(nqubits, instance)
nqubits = int(control[0])
times = functions.times(nqubits, clauses)
sh, smap = functions.h_problem(nqubits, clauses)
Q, constant = functions.symbolic_to_dwave(sh, smap)
model = dimod.BinaryQuadraticModel.from_qubo(Q, offset = 0.0)
if not chainstrength:
chainstrength = dwave.embedding.chain_strength.uniform_torque_compensation(model)
print(f'Automatic chain strength: {chainstrength}\n')
else:
print(f'Chosen chain strength: {chainstrength}\n')
if solution:
print(f'Target solution that solves the problem: {" ".join(solution)}\n')
sampler = EmbeddingComposite(DWaveSampler())
if greedy:
solver_greedy = SteepestDescentSolver()
sampleset = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='raw')
response = solver_greedy.sample(model, initial_states=sampleset)
else:
response = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='histogram')
best_sample = response.record.sample[0]
best_energy = response.record.energy[0]
print(f'Best result found: {best_sample}\n')
print(f'With energy: {best_energy+constant}\n')
good_samples = response.record.sample[:min(len(response.record.sample), nqubits)]
good_energies = response.record.energy[:min(len(response.record.energy), nqubits)]
print(f'The best {len(good_samples)} samples found in the evolution are:\n')
for i in range(len(good_samples)):
print(f'Sample: {good_samples[i]} with energy: {good_energies[i]+constant}\n')
if inspect:
dwave.inspector.show(response)
class SteepestDescentProblemSampler(traits.ProblemSampler, traits.SISO, Runnable):
"""A steepest descent solver for a complete problem.
Args:
num_reads (int, optional, default=len(state.samples) or 1):
Number of states (output solutions) to read from the sampler.
initial_states_generator (str, 'none'/'tile'/'random', optional, default='random'):
Defines the expansion of input state samples into `initial_states`
for the steepest descent, if fewer than `num_reads` samples are
present. See :meth:`greedy.sampler.SteepestDescentSolver.sample`.
"""
def __init__(self, num_reads=None, initial_states_generator='random', **runopts):
super(SteepestDescentProblemSampler, self).__init__(**runopts)
self.num_reads = num_reads
self.initial_states_generator = initial_states_generator
self.sampler = SteepestDescentSolver()
def __repr__(self):
return ("{self}(num_reads={self.num_reads!r}, "
"initial_states_generator={self.initial_states_generator!r})").format(self=self)
def next(self, state, **runopts):
samples = self.sampler.sample(
state.problem, num_reads=self.num_reads,
initial_states=state.samples,
initial_states_generator=self.initial_states_generator)
return state.updated(samples=samples)
def get_sampler(self, profile, solver):
"Return a dimod.Sampler object and associated solver information."
# Handle built-in software samplers as special cases.
info = {}
if solver != None:
info["solver_name"] = solver
if solver == "exact":
return ExactSolver(), info, {}
elif solver == "neal":
return SimulatedAnnealingSampler(), info, {}
elif solver == "tabu":
return TabuSampler(), info, {}
elif solver == "greedy":
return SteepestDescentSolver(), info, {}
elif solver == "kerberos" or (solver != None and solver[:9] == "kerberos,"):
base_sampler = KerberosSampler()
try:
sub_sampler_name = solver.split(",")[1]
except IndexError:
sub_sampler_name = None
sub_sampler, sub_info, params = self.get_sampler_from_config(profile, sub_sampler_name, "qpu")
info.update(self._recursive_properties(sub_sampler))
info["solver_name"] = "kerberos + %s" % sub_info["solver_name"]
params["qpu_sampler"] = sub_sampler
return base_sampler, info, params
elif solver == "qbsolv" or (solver != None and solver[:7] == "qbsolv,"):
base_sampler = QBSolv()
try:
sub_sampler_name = solver.split(",")[1]
except IndexError:
sub_sampler_name = None
sub_sampler, sub_info, params = self.get_sampler(profile, sub_sampler_name)
if getattr(sub_sampler, "structure", None) != None:
sub_sampler = EmbeddingComposite(sub_sampler)
info.update(self._recursive_properties(sub_sampler))
info["solver_name"] = "QBSolv + %s" % sub_info["solver_name"]
params["solver"] = sub_sampler
return base_sampler, info, params
# In the common case, read the configuration file, either the
# default or the one named by the DWAVE_CONFIG_FILE environment
# variable.
return self.get_sampler_from_config(profile, solver)
def main(bits, T, chainstrength, numruns, control, greedy, inspect, repeat):
"""
Args:
nqubits (int): number of qubits for the file that contains the
information of an Exact Cover instance.
instance (int): intance used for the desired number of qubits.
T (float):
Returns:
"""
x = symbols(' '.join((f'x{i}' for i in range(1, bits+1))))
if bits == 3:
ancillas = (symbols(' '.join((f'xa{i}' for i in range(1, 2)))), )
f = functions.f_3(*x)
r = functions.r_3()
elif bits == 8:
ancillas = symbols(' '.join((f'xa{i}' for i in range(1, 23))))
f = functions.f_8(*x)
r = functions.r_8()
else:
raise ValueError('Only instances for 3 and 8 bits are supported.\n')
sym = x+ancillas
symbol_num = {}
for i in range(len(sym)):
symbol_num[sym[i]] = i
print(f'Solving a {bits} bit instance of a nonlinear function using Adiabatic Evolution.\n')
print('Creating problem Hamiltonian.\n')
h = functions.to_hamiltonian(f, r, sym)
terms = functions.check_interactions(h)
print('Number of terms for each k-body interactions.\n')
print(terms, '\n')
print('Reducing the k-body interactions using ancillas.\n')
if control:
h2, anc = functions.to_gadget_ruge2(h, sym, ancillas, bits)
else:
h2, anc = functions.to_gadget_ruge1(h, sym, ancillas, bits)
terms = functions.check_interactions(h2, high=False)
print(f'Total number of qubits needed for the 2-local hamiltonian encoding {bits} bits: {anc+bits}.\n')
print('Number of terms for each k-body interactions after gadget aplication.\n')
print(terms, '\n')
functions.check_two_body(h2)
Q, constant = functions.symbolic_to_dwave(h2, symbol_num)
model = dimod.BinaryQuadraticModel.from_qubo(Q, offset = 0.0)
if not chainstrength:
chainstrength = 0
for i in Q.values():
chainstrength += abs(i)
chainstrength *= 3/len(Q)
#chainstrength = dwave.embedding.chain_strength.uniform_torque_compensation(model)
print(f'Automatic chain strength: {chainstrength}\n')
else:
print(f'Chosen chain strength: {chainstrength}\n')
sampler = EmbeddingComposite(DWaveSampler())
if greedy:
solver_greedy = SteepestDescentSolver()
sampleset = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='raw')
response = solver_greedy.sample(model, initial_states=sampleset)
else:
response = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='histogram')
record = response.record
order = np.argsort(record['energy'])
best_sample = record.sample[order[0]]
best_energy = record.energy[order[0]]
print(f'Best result found:\n')
print(f'Relevant bits: {best_sample[:bits]}\n')
print(f'Ancillas: {best_sample[bits:]}\n')
print(f'With energy: {best_energy+constant}\n')
print(f'The best {min(len(record.sample), bits)} samples found in the evolution are:\n')
for i in range(min(len(record.sample), bits)):
print(f'Bits: {record.sample[order[i]][:bits]} Ancillas: {record.sample[order[i]][bits:]} with energy: {record.energy[order[i]]+constant}\n')
if inspect:
dwave.inspector.show(response)
fix = []
out = []
if repeat:
for i in range(3):
c = 0
for j in range(len(sym)):
if j in fix:
continue
else:
a = True
b = record.sample[order[0]][c]
for k in range(min(len(record.sample), bits)):
if b != record.sample[order[k]][c]:
a = False
if a:
fix.append(j)
out.append(b)
c += 1
print(f'The same value was found in positions {fix} \n')
print(f'with values {out}.\n')
for j in range(len(fix)):
h2 = h2.subs(sym[fix[j]], out[j])
terms = functions.check_interactions(h2, high=False)
print(f'Total number of qubits needed for the next step: {bits+anc-len(fix)}.\n')
print('Number of terms for each k-body interactions after gadget aplication.\n')
print(terms, '\n')
Q, constant = functions.symbolic_to_dwave(h2, symbol_num)
model = dimod.BinaryQuadraticModel.from_qubo(Q, offset = 0.0)
if not chainstrength:
chainstrength = 0
for i in Q.values():
chainstrength += abs(i)
chainstrength *= 3/len(Q)
#chainstrength = dwave.embedding.chain_strength.uniform_torque_compensation(model)
print(f'Automatic chain strength: {chainstrength}\n')
else:
print(f'Chosen chain strength: {chainstrength}\n')
sampler = EmbeddingComposite(DWaveSampler())
if greedy:
solver_greedy = SteepestDescentSolver()
sampleset = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='raw')
response = solver_greedy.sample(model, initial_states=sampleset)
else:
response = sampler.sample(model, chain_strength=chainstrength, num_reads=numruns, annealing_time=T, answer_mode='histogram')
record = response.record
order = np.argsort(record['energy'])
best_sample = record.sample[order[0]]
best_energy = record.energy[order[0]]
print(f'Best result found: {best_sample}\n')
print(f'With energy: {best_energy+constant}\n')
print(f'The best {min(len(record.sample), bits)} samples found in the evolution are:\n')
for i in range(min(len(record.sample), bits)):
print(f'Result: {record.sample[order[i]]} with energy: {record.energy[order[i]]+constant}\n')
print('Reconstructing state...\n')
c = 0
result = []
for i in range(len(sym)):
if i in fix:
result.append(out[fix.index(i)])
else:
result.append(best_sample[c])
c += 1
print(f'Reconstructed result:\n')
print(f'Relevant bits: {result[:bits]}\n')
print(f'Ancillas: {result[bits:]}\n')
for i in range(bits):
h = h.subs(sym[i], result[i])
print(f'With total energy: {h}\n')