def main(N=4):
"""
This is the main function which maps the classical problem to the quantum version solved by QAOA and outputs
the quantum solution and its corresponding classical ones. Here, N=4 is a 4-qubit example to show how QAOA works.
"""
# Generate the adjacency matrix from the description of the problem-based graph
_, classical_graph_adjacency = generate_graph(N, 1)
Paddle_QAOA(classical_graph_adjacency)
def benchmark_QAOA(classical_graph_adjacency=None, N=None):
"""
This function benchmarks the performance of QAOA. Indeed, it compares its approximate solution obtained
from QAOA with predetermined parameters, such as iteration step = 120 and learning rate = 0.1, to the exact solution
to the classical problem.
"""
# Generate the graph and its adjacency matrix from the classical problem, such as the Max-Cut problem
if all(var is None for var in (classical_graph_adjacency, N)):
N = 4
_, classical_graph_adjacency = generate_graph(N, 1)
# Convert the Hamiltonian's list form to matrix form
H_matrix = pauli_str_to_matrix(H_generator(N, classical_graph_adjacency),
N)
H_diag = diag(H_matrix).real
# Compute the exact solution of the original problem to benchmark the performance of QAOA
H_max = max(H_diag)
H_min = min(H_diag)
print('H_max:', H_max, ' H_min:', H_min)
# Load the data of QAOA
x1 = load('./output/summary_data.npz')
H_min = ones([len(x1['iter'])]) * H_min
# Plot it
pyplot.figure(1)
loss_QAOA, = pyplot.plot(x1['iter'],
x1['energy'],
alpha=0.7,
marker='',
linestyle="--",
linewidth=2,
color='m')
benchmark, = pyplot.plot(x1['iter'],
H_min,
alpha=0.7,
marker='',
linestyle=":",
linewidth=2,
color='b')
pyplot.xlabel('Number of iteration')
pyplot.ylabel('Performance of the loss function for QAOA')
pyplot.legend(
handles=[loss_QAOA, benchmark],
labels=[
r'Loss function $\left\langle {\psi \left( {\bf{\theta }} \right)} '
r'\right|H\left| {\psi \left( {\bf{\theta }} \right)} \right\rangle $',
'The benchmark result',
],
loc='best')
# Show the picture
pyplot.show()
def main(N=4):
"""
QAOA Main
"""
classical_graph, classical_graph_adjacency = generate_graph(N, 1)
print(classical_graph_adjacency)
prob_measure = Paddle_QAOA(classical_graph_adjacency)
# Flatten array[[]] to []
prob_measure = prob_measure.flatten()
# Plot it!
plot_graph(prob_measure, classical_graph, N)
def main(N=4):
# number of qubits or number of nodes in the graph
N = 4
classical_graph, classical_graph_adjacency = generate_graph(N,
GRAPHMETHOD=1)
print(classical_graph_adjacency)
# Convert the Hamiltonian's list form to matrix form
H_matrix = pauli_str_to_matrix(H_generator(N, classical_graph_adjacency),
N)
H_diag = np.diag(H_matrix).real
H_max = np.max(H_diag)
H_min = np.min(H_diag)
print(H_diag)
print('H_max:', H_max, ' H_min:', H_min)
pos = nx.circular_layout(classical_graph)
nx.draw(classical_graph,
pos,
width=4,
with_labels=True,
font_weight='bold')
plt.show()
classical_graph, classical_graph_adjacency = generate_graph(N, 1)
opt_cir = Paddle_QAOA(classical_graph_adjacency,
N=4,
P=4,
METHOD=1,
ITR=120,
LR=0.1)
# Load the data of QAOA
x1 = np.load('./output/summary_data.npz')
H_min = np.ones([len(x1['iter'])]) * H_min
# Plot loss
loss_QAOA, = plt.plot(x1['iter'],
x1['energy'],
alpha=0.7,
marker='',
linestyle="--",
linewidth=2,
color='m')
benchmark, = plt.plot(x1['iter'],
H_min,
alpha=0.7,
marker='',
linestyle=":",
linewidth=2,
color='b')
plt.xlabel('Number of iteration')
plt.ylabel('Performance of the loss function for QAOA')
plt.legend(
handles=[loss_QAOA, benchmark],
labels=[
r'Loss function $\left\langle {\psi \left( {\bf{\theta }} \right)} '
r'\right|H\left| {\psi \left( {\bf{\theta }} \right)} \right\rangle $',
'The benchmark result',
],
loc='best')
# Show the plot
plt.show()
with fluid.dygraph.guard():
# Measure the output state of the QAOA circuit for 1024 shots by default
prob_measure = opt_cir.measure(plot=True)
# Find the max value in measured probability of bitstrings
max_prob = max(prob_measure.values())
# Find the bitstring with max probability
solution_list = [
result[0] for result in prob_measure.items() if result[1] == max_prob
]
print("The output bitstring:", solution_list)
# Draw the graph representing the first bitstring in the solution_list to the MaxCut-like problem
head_bitstring = solution_list[0]
node_cut = [
"blue" if head_bitstring[node] == "1" else "red"
for node in classical_graph
]
edge_cut = [
"solid"
if head_bitstring[node_row] == head_bitstring[node_col] else "dashed"
for node_row, node_col in classical_graph.edges()
]
nx.draw(
classical_graph,
pos,
node_color=node_cut,
style=edge_cut,
width=4,
with_labels=True,
font_weight="bold",
)
plt.show()
