def _get_qaoa_circuits_to_compile(graph_type,
graph_N,
graph_p,
blockings,
sampling_rate=None):
assert graph_p in [1, 2, 3, 4, 5, 6, 7, 8], 'we only did p = 1...8'
set_seeds()
circuit = get_qaoa_circuit(graph_N, graph_p, graph_type)
circuit = optimize_circuit(circuit)
coupling_list = get_nearest_neighbor_coupling_list(2, int(graph_N / 2))
circuit = optimize_circuit(circuit, coupling_list)
slice_circuits_list = _get_slice_circuits_list(circuit, blockings)
return _get_circuits_to_compile(slice_circuits_list,
sampling_rate=sampling_rate)
use_gpu=False,
sparse_H=False,
method='Adam',
maxA=maxA,
show_plots=False,
file_name=file_name,
data_path=data_path)
if SS.l < SS.conv.conv_target: # if converged, search lower half
max_steps = mid_steps
else:
min_steps = mid_steps
return mid_steps / 20
qaoa_circuit = qaoa.get_qaoa_circuit(graph_N, graph_p, 'ErdosRenyi')
qaoa_circuit = util.circuitutil.optimize_circuit(qaoa_circuit)
coupling_list = util.circuitutil.get_nearest_neighbor_coupling_list(
2, int(graph_N / 2))
qaoa_circuit = util.circuitutil.optimize_circuit(qaoa_circuit, coupling_list)
from qiskit import QuantumCircuit, QuantumRegister
from copy import deepcopy
def indices(gate):
return [qarg[1] for qarg in gate.qargs]
def gate_block_index(gate, blocking):
if len(indices(gate)) == 1:
use_gpu=False,
sparse_H=False,
method='Adam',
maxA=maxA,
show_plots=False,
file_name=file_name,
data_path=data_path)
if SS.l < SS.conv.conv_target: # if converged, search lower half
max_steps = mid_steps
else:
min_steps = mid_steps
return mid_steps / 20
qaoa_circuit = qaoa.get_qaoa_circuit(graph_N, graph_p, '3Reg')
qaoa_circuit = util.circuitutil.optimize_circuit(qaoa_circuit)
coupling_list = util.circuitutil.get_nearest_neighbor_coupling_list(
2, int(graph_N / 2))
qaoa_circuit = util.circuitutil.optimize_circuit(qaoa_circuit, coupling_list)
from qiskit import QuantumCircuit, QuantumRegister
from copy import deepcopy
def indices(gate):
return [qarg[1] for qarg in gate.qargs]
def gate_block_index(gate, blocking):
if len(indices(gate)) == 1:
col = int(N / 2)
d = 2 # this is the number of energy levels to consider (i.e. d-level qudits)
max_iterations = (2**N) * 125
decay = max_iterations / 2
convergence = {
'rate': 0.01,
'max_iterations': max_iterations,
'conv_target': 1e-3,
'learning_rate_decay': decay,
'min_grad': 1e-9,
'update_step': 20
}
reg_coeffs = {}
for p in range(1, 3):
print("Running 3-regular graph N=%d, p=%d" % (N, p), flush=True)
circuit = qaoa.get_qaoa_circuit(N, p, '3Reg')
# TODO: average over 10 graphs
U = util.circuitutil.get_unitary(circuit)
connected_qubit_pairs = util.get_nearest_neighbor_coupling_list(
row, col, directed=False)
H0 = hamiltonian.get_H0(N, d)
Hops, Hnames = hamiltonian.get_Hops_and_Hnames(N, d,
connected_qubit_pairs)
states_concerned_list = hamiltonian.get_full_states_concerned_list(
N, d)
maxA = hamiltonian.get_maxA(N, d, connected_qubit_pairs)
shortest_time = binary_search_for_shortest_pulse_time(
10, int(90 * (N / 5) * p), convergence, reg_coeffs)
full_times.append(shortest_time)
print(full_times, flush=True)
