def CreateLabelViaAstar(CNOT_list, G, q_phy, q_log, shortest_length_G, possible_swap_combination):
'''
create minimum SWAP cost for a single layer via Astar
'''
DG = ct.OperationToDependencyGraph(CNOT_list)
cir_phy = QuantumCircuit(q_phy)
initial_map = Map(q_log, G)
res = ct.AStarSearch(q_phy, cir_phy, G, DG, initial_map, shortest_length_G,
possible_swap_combination=possible_swap_combination)
return res[0]
def CreateLabelViaZHOU(CNOT_list, G, q_phy, q_log, shortest_length_G, shortest_path_G):
'''
create minimum SWAP cost for a single layer via our proposed method
'''
DG = ct.OperationToDependencyGraph(CNOT_list)
cir_phy = QuantumCircuit(q_phy)
initial_map = Map(q_log, G)
res = ct.RemoteCNOTandWindowLookAhead(q_phy, cir_phy, G, DG, initial_map,
shortest_length_G, shortest_path_G,
depth_lookahead=1, use_prune=False,
draw=False, DiG=None, level_lookahead=None)
return res[0]
shortest_length_G = dict(nx.shortest_path_length(G, source=None, target=None, weight=None, method='dijkstra'))
if draw_architecture_graph == True: nx.draw(G, with_labels=True)
'''
'''generate CNOT operation'''
total_CNOT = res_qasm[1][3]
'''generate dependency graph'''
DG = res_qasm[1][0]
if draw_DG == True: nx.draw(DG, with_labels=True)
'''generate party map for CNOT circuits'''
if use_steiner_tree_and_remoteCNOT == 1 or use_UDecompositionFullConnectivity == 1 or use_UDecompositionFullConnectivityPATEL == 1:
party_map = np.eye(num_vertex)
ct.PerformOperationCNOTinPartyMap(party_map, total_CNOT)
new_G = ct.AllocateVertexToPartyMap(G, num_vertex)
'''initialize map from logical qubits to physical qubits'''
initial_map_list = results[file[0:-5]]['initial map'][repeat]
initial_map = Map(q_log, G, initial_map_list)
'''draw logical quantum circuits'''
if draw_logical_circuit == True: print(cir_log.draw())
'''search using specific methods'''
if use_naive_search == True:
cost_naive = ct.NaiveSearch(q_phy, QuantumCircuit(q_phy), G,
copy.deepcopy(DG), initial_map,
shortest_path_G,
draw_physical_circuit_niave)
y_label_naive.append(cost_naive)
if use_HeuristicGreedySearch == True:
cost_HeuristicGreedySearch = ct.HeuristicGreedySearch(
q_phy, QuantumCircuit(q_phy), G, copy.deepcopy(DG),
initial_map, shortest_length_G, possible_swap_combination,
draw_physical_circuit_HeuristicGreedySearch)
y_label_HeuristicGreedySearch.append(cost_HeuristicGreedySearch)
total_CNOT = res_qasm[1][3]
'''generate dependency graph'''
DG = res_qasm[1][0]
if draw_DG == True: nx.draw(DG, with_labels=True)
'''generate party map for CNOT circuits'''
if use_steiner_tree_and_remoteCNOT == 1 or use_UDecompositionFullConnectivity ==1 or use_UDecompositionFullConnectivityPATEL ==1:
party_map = np.eye(num_vertex)
ct.PerformOperationCNOTinPartyMap(party_map, total_CNOT)
new_G = ct.AllocateVertexToPartyMap(G, num_vertex)
'''initialize map from logical qubits to physical qubits'''
'''1-1, 2-2 ...'''
if initial_mapping_control == 0: initial_map = Map(q_log, G)
'''for circuit with only 10 qubit, we mannually map last 5 qubits to the down line'''
if initial_mapping_control == 2:
initial_map = Map(q_log, G)
initial_map.RenewMapViaExchangeCod(9, 15)
initial_map.RenewMapViaExchangeCod(8, 14)
initial_map.RenewMapViaExchangeCod(7, 13)
initial_map.RenewMapViaExchangeCod(6, 12)
'''specific initial map through vertex list in AG'''
# =============================================================================
# initial_map = Map(q_log, G, [1,2,3,8,7,6,11,12,13,16,17,18,4,9,14,19])
# =============================================================================
'''optimized initial mapping'''
if initial_mapping_control == 1:
map_res = ct.FindInitialMapping(DG, q_log, G, shortest_length_G[0])
initial_map = map_res[0]
'''calculate shortest path and its length'''
'''
shortest_path_G = nx.shortest_path(G, source=None, target=None, weight=None, method='dijkstra')
shortest_length_G = dict(nx.shortest_path_length(G, source=None, target=None, weight=None, method='dijkstra'))
if draw_architecture_graph == True: nx.draw(G, with_labels=True)
'''
'''generate CNOT operation'''
total_CNOT = res_qasm[1][3]
'''generate dependency graph'''
DG = res_qasm[1][0]
if draw_DG == True: nx.draw(DG, with_labels=True)
'''initialize map from logical qubits to physical qubits'''
'''1-1, 2-2 ...'''
if initial_mapping_control == 0:
t_s = time.time()
initial_map = Map(q_log, G)
t_e = time.time()
'''for circuit with only 10 qubit, we mannually map last 5 qubits to the down line'''
if initial_mapping_control == 2:
initial_map = Map(q_log, G)
initial_map.RenewMapViaExchangeCod(9, 15)
initial_map.RenewMapViaExchangeCod(8, 14)
initial_map.RenewMapViaExchangeCod(7, 13)
initial_map.RenewMapViaExchangeCod(6, 12)
'''specific initial map through vertex list in AG'''
# =============================================================================
# initial_map = Map(q_log, G, [1,2,3,8,7,6,11,12,13,16,17,18,4,9,14,19])
# =============================================================================
'''optimized initial mapping'''
if initial_mapping_control == 1:
t_s = time.time()
'''
'''calculate shortest path and its length'''
'''
shortest_path_G = nx.shortest_path(G, source=None, target=None, weight=None, method='dijkstra')
shortest_length_G = dict(nx.shortest_path_length(G, source=None, target=None, weight=None, method='dijkstra'))
if draw_architecture_graph == True: nx.draw(G, with_labels=True)
'''
'''generate CNOT operation randomly'''
total_CNOT = ct.CreateCNOTRandomly(q_log, num_CNOT, cir_log)
'''generate party map for CNOT circuits'''
if use_steiner_tree_and_remoteCNOT == 1 or use_UDecompositionFullConnectivity == 1 or use_UDecompositionFullConnectivityPATEL == 1:
party_map = np.eye(num_vertex)
ct.PerformOperationCNOTinPartyMap(party_map, total_CNOT)
new_G = ct.AllocateVertexToPartyMap(G, num_vertex)
'''initialize map from logical qubits to physical qubits'''
initial_map = Map(q_log, G)
'''generate dependency graph'''
DG = ct.OperationToDependencyGraph(total_CNOT)
if draw_DG == True: nx.draw(DG, with_labels=True)
'''draw logical quantum circuits'''
if draw_logical_circuit == True: print(cir_log.draw())
'''search using specific methods'''
if use_naive_search == True:
cost_naive = ct.NaiveSearch(q_phy, QuantumCircuit(q_phy), G,
copy.deepcopy(DG), initial_map,
shortest_path_G,
draw_physical_circuit_niave)
y_label_naive.append(cost_naive)
total_naive = total_naive + cost_naive
if use_HeuristicGreedySearch == True:
cost_HeuristicGreedySearch = ct.HeuristicGreedySearch(