def simulate_state(self):
n_qubits = self.n_qubits
circuit = self.qc
buckets, data_dict, bra_vars, ket_vars = qtree.optimizer.circ2buckets(
n_qubits, circuit)
graph = qtree.graph_model.buckets2graph(buckets,
ignore_variables=ket_vars+bra_vars)
peo_ints, treewidth = utils.get_locale_peo(graph, utils.n_neighbors)
peo = [qtree.optimizer.Var(var, size=graph.nodes[var]['size'],
name=graph.nodes[var]['name'])
for var in peo_ints]
peo = ket_vars + bra_vars + peo
perm_buckets, perm_dict = qtree.optimizer.reorder_buckets(buckets, peo)
ket_vars = sorted([perm_dict[idx] for idx in ket_vars], key=str)
bra_vars = sorted([perm_dict[idx] for idx in bra_vars], key=str)
initial_state = target_state = 0
slice_dict = qtree.utils.slice_from_bits(initial_state, ket_vars)
slice_dict.update(
qtree.utils.slice_from_bits(target_state, bra_vars)
)
sliced_buckets = qtree.np_framework.get_sliced_np_buckets(
perm_buckets, data_dict, slice_dict)
result = qtree.optimizer.bucket_elimination(
sliced_buckets, qtree.np_framework.process_bucket_np)
return result
def simulate_circ(circuit, n_qubits):
buckets, data_dict, bra_vars, ket_vars = qtree.optimizer.circ2buckets(
n_qubits, circuit)
graph = qtree.graph_model.buckets2graph(
buckets,
ignore_variables=bra_vars+ket_vars)
peo, nghs = utils.get_locale_peo(graph, utils.n_neighbors)
peo = qtree.graph_model.indices_to_vars(peo, graph)
# place bra and ket variables to beginning, so these variables
# will be contracted first
#peo, treewidth = qtree.graph_model.get_peo(graph)
peo = ket_vars + bra_vars + peo
perm_buckets, perm_dict = qtree.optimizer.reorder_buckets(buckets, peo)
# extract bra and ket variables from variable list and sort according
# to qubit order
ket_vars = sorted([perm_dict[idx] for idx in ket_vars], key=str)
bra_vars = sorted([perm_dict[idx] for idx in bra_vars], key=str)
# Take the subtensor corresponding to the initial state
initial_state = target_state = 0
slice_dict = qtree.utils.slice_from_bits(initial_state, ket_vars)
slice_dict.update(
qtree.utils.slice_from_bits(target_state, bra_vars)
)
sliced_buckets = qtree.np_framework.get_sliced_np_buckets(
perm_buckets, data_dict, slice_dict)
with timing('time_raw', callback=append_to(profile)):
result = qtree.optimizer.bucket_elimination(
sliced_buckets, qtree.np_framework.process_bucket_np)
def get_qbb_peo(graph):
try:
peo, tw = qtree.graph_model.get_peo(graph)
fail = False
except:
print('QBB fail, nodes count:', graph.number_of_nodes())
peo, nghs = utils.get_locale_peo(graph, utils.n_neighbors)
fail = True
return peo, fail
def optimize_order(graph, ordering_algo='qbb'):
if ordering_algo == 'qbb':
peo, tw = qtree.graph_model.get_peo(graph, int_vars=True)
if ordering_algo == 'nghs':
peo, _nghs = utils.get_locale_peo(graph, utils.n_neighbors)
tw = max(_nghs) - 1
mapping = {v: i for i, v in enumerate(peo)}
tensors = expr.get_tensors_from_graph(graph)
reorder_tensors(tensors, peo)
graph = nx.relabel_nodes(graph, mapping, copy=True)
return peo, tw, graph
def _optimise_graph(graph):
peo, nghs = utils.get_locale_peo(graph, utils.n_neighbors)
graph_opt, slice_dict = utils.reorder_graph(graph, peo)
return graph_opt, nghs
plt.savefig('figures/rect_cost_vs_nodes_T_p1.png')
# -
# # Finding biggest tacklable task
# ## Full ordering
# +
graph, N = qaoa.get_test_expr_graph(25, 1, type='randomreg', degree=3)
print(N)
# -
# ### Naive (degree-based local) peo
# %%time
peo, nghs = utils.get_locale_peo(graph, utils.n_neighbors)
graph_relabel, slice_dict = utils.reorder_graph(graph, peo)
# +
costs, flops = qtree.graph_model.cost_estimator(graph_relabel)
print(max(costs)/1e9)
print(max(flops)/1e9)
utils.plot_cost(costs, flops)
#nx.draw_kamada_kawai(graph, node_size=3)
# -
neigh_plot(nghs)
def n_peo(graph):
return utils.get_locale_peo(graph, utils.n_neighbors)