def _optimize(self, c):
g = c.to_graph()
zx.full_reduce(g, quiet=True)
g.normalize()
gs = pivot_anneal(g, iters=5, cool=0.0025, score=self.twoq_score)
c_opt = zx.extract_circuit(gs)
c_opt = self.post_process(c_opt)
return c_opt
def sa_with_restarts(g, n_restarts=3, n_iters=2500, quiet=True):
best_g = g.copy()
best_score = g_score(best_g)
for i in range(n_restarts):
g_sa, _ = pivot_anneal(g, iters=n_iters, quiet=quiet)
trial_score = g_score(g_sa)
if trial_score < best_score or i == 0:
best_score = trial_score
best_g = g_sa.copy()
return best_g
g_tmp = g.copy()
if REDUCE_METHOD == "TR":
zx.teleport_reduce(g_tmp)
c_tr = zx.Circuit.from_graph(g_tmp.copy()).to_basic_gates()
c_tr = zx.basic_optimization(c_tr)
g_simp = c_tr.to_graph()
elif REDUCE_METHOD == "FR":
zx.full_reduce(g_tmp)
g_simp = g_tmp.copy()
else:
raise RuntimeError(f"Invalid REDUCE_METHOD: {REDUCE_METHOD}")
to_graph_like(g_simp)
_, scores = pivot_anneal(g_simp, iters=N_ITERS)
final_score = scores[-1]
for x in range(0, N_ITERS, INTERVAL):
if final_score < scores[x]:
improvement_after[x].append(1)
else:
improvement_after[x].append(0)
pdb.set_trace()
improvement_after_probs = {k: np.mean(v) for k, v in improvement_after.items()}
xs = list(improvement_after_probs.keys())
ys = list(improvement_after_probs.values())
plt.rcParams.update({'font.size': 14})
plt.scatter(xs, ys)
axes[0].plot(list(range(len(reductions))), reductions)
axes[0].set_xlabel("Generation")
axes[0].set_ylabel("Reduction")
axes[0].set_title(f"GA: {N_MUTANTS} mutants, {N_GENS} generations")
axes[0].legend()
## SA part
reductions = list()
N_TRIALS = 10
N_ITERS = 1000
init_score = 4 * c_tr.twoqubitcount() + c_tr.tcount()
for i in tqdm(range(N_TRIALS)):
g_anneal, _ = sa.pivot_anneal(g_tr.copy(),
iters=N_ITERS,
score=sa.c_score)
zx.full_reduce(g_anneal)
c_anneal = zx.extract_circuit(g_anneal.copy()).to_basic_gates()
c_anneal = zx.basic_optimization(c_anneal)
trial_score = 4 * c_anneal.twoqubitcount() + c_anneal.tcount()
reduction = (init_score - trial_score) / init_score * 100
reductions.append(reduction)
# print(f"\n----- trial {i} -----")
# print(c_anneal.stats())
"""
plt.hist(reductions)
plt.xlabel("Reduction")
plt.ylabel("Frequency")