Note
This page was generated from tutorials/algorithms/05_qaoa.ipynb.
Quantum Approximate Optimization AlgorithmΒΆ
Qiskit has an implementation of the Quantum Approximate Optimization Algorithm QAOA and this notebook demonstrates using it for a graph partition problem.
[1]:
import numpy as np
import networkx as nx
from qiskit import Aer
from qiskit.algorithms import NumPyMinimumEigensolver
First we create a graph and draw it so it can be seen.
[2]:
num_nodes = 4
w = np.array([[0., 1., 1., 0.],
[1., 0., 1., 1.],
[1., 1., 0., 1.],
[0., 1., 1., 0.]])
G = nx.from_numpy_matrix(w)
[3]:
layout = nx.random_layout(G, seed=10)
colors = ['r', 'g', 'b', 'y']
nx.draw(G, layout, node_color=colors)
labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos=layout, edge_labels=labels);
The brute-force method is as follows. Basically, we exhaustively try all the binary assignments. In each binary assignment, the entry of a vertex is either 0 (meaning the vertex is in the first partition) or 1 (meaning the vertex is in the second partition). We print the binary assignment that satisfies the definition of the graph partition and corresponds to the minimal number of crossing edges.
[4]:
def objective_value(x, w):
X = np.outer(x, (1 - x))
w_01 = np.where(w != 0, 1, 0)
return np.sum(w_01 * X)
def brute_force():
# use the brute-force way to generate the oracle
def bitfield(n, L):
result = np.binary_repr(n, L)
return [int(digit) for digit in result] # [2:] to chop off the "0b" part
L = num_nodes
max = 2**L
minimal_v = np.inf
for i in range(max):
cur = bitfield(i, L)
how_many_nonzero = np.count_nonzero(cur)
if how_many_nonzero * 2 != L: # not balanced
continue
cur_v = objective_value(np.array(cur), w)
if cur_v < minimal_v:
minimal_v = cur_v
return minimal_v
sol = brute_force()
print(f'Objective value computed by the brute-force method is {sol}')
Objective value computed by the brute-force method is 3
The graph partition problem can be converted to an Ising Hamiltonian. Qiskit has different capabilities in the Optimization module to do this. Here, since the goal is to show QAOA, the module is used without further explanation to create the operator. The paper Ising formulations of many NP problems may be of interest if you would like to understand the technique further.
[5]:
from qiskit.quantum_info import Pauli
from qiskit.opflow import PauliSumOp
def get_operator(weight_matrix):
r"""Generate Hamiltonian for the graph partitioning
Notes:
Goals:
1 separate the vertices into two set of the same size
2 make sure the number of edges between the two set is minimized.
Hamiltonian:
H = H_A + H_B
H_A = sum\_{(i,j)\in E}{(1-ZiZj)/2}
H_B = (sum_{i}{Zi})^2 = sum_{i}{Zi^2}+sum_{i!=j}{ZiZj}
H_A is for achieving goal 2 and H_B is for achieving goal 1.
Args:
weight_matrix (numpy.ndarray) : adjacency matrix.
Returns:
PauliSumOp: operator for the Hamiltonian
float: a constant shift for the obj function.
"""
num_nodes = len(weight_matrix)
pauli_list = []
shift = 0
for i in range(num_nodes):
for j in range(i):
if weight_matrix[i, j] != 0:
x_p = np.zeros(num_nodes, dtype=bool)
z_p = np.zeros(num_nodes, dtype=bool)
z_p[i] = True
z_p[j] = True
pauli_list.append([-0.5, Pauli((z_p, x_p))])
shift += 0.5
for i in range(num_nodes):
for j in range(num_nodes):
if i != j:
x_p = np.zeros(num_nodes, dtype=bool)
z_p = np.zeros(num_nodes, dtype=bool)
z_p[i] = True
z_p[j] = True
pauli_list.append([1, Pauli((z_p, x_p))])
else:
shift += 1
pauli_list = [(pauli[1].to_label(), pauli[0]) for pauli in pauli_list]
return PauliSumOp.from_list(pauli_list), shift
qubit_op, offset = get_operator(w)
So lets use the QAOA algorithm to find the solution.
[6]:
from collections import OrderedDict
from qiskit.utils import algorithm_globals
from qiskit.algorithms import QAOA
from qiskit.opflow import StateFn
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
def sample_most_likely(state_vector):
"""Compute the most likely binary string from state vector.
Args:
state_vector (numpy.ndarray or dict): state vector or counts.
Returns:
numpy.ndarray: binary string as numpy.ndarray of ints.
"""
if isinstance(state_vector, (OrderedDict, dict)):
# get the binary string with the largest count
binary_string = sorted(state_vector.items(), key=lambda kv: kv[1])[-1][0]
x = np.asarray([int(y) for y in reversed(list(binary_string))])
return x
elif isinstance(state_vector, StateFn):
binary_string = list(state_vector.sample().keys())[0]
x = np.asarray([int(y) for y in reversed(list(binary_string))])
return x
else:
n = int(np.log2(state_vector.shape[0]))
k = np.argmax(np.abs(state_vector))
x = np.zeros(n)
for i in range(n):
x[i] = k % 2
k >>= 1
return x
def objective_value(x, w):
"""Compute the value of a cut.
Args:
x (numpy.ndarray): binary string as numpy array.
w (numpy.ndarray): adjacency matrix.
Returns:
float: value of the cut.
"""
X = np.outer(x, (1 - x))
w_01 = np.where(w != 0, 1, 0)
return np.sum(w_01 * X)
algorithm_globals.random_seed = 10598
optimizer = COBYLA()
qaoa = QAOA(optimizer, quantum_instance=Aer.get_backend('statevector_simulator'))
result = qaoa.compute_minimum_eigenvalue(qubit_op)
x = sample_most_likely(result.eigenstate)
print(x)
print(f'Objective value computed by QAOA is {objective_value(x, w)}')
[1. 0. 1. 0.]
Objective value computed by QAOA is 3.0
The outcome can be seen to match to the value computed above by brute force. But we can also use the classical NumPyMinimumEigensolver to do the computation, which may be useful as a reference without doing things by brute force.
[7]:
npme = NumPyMinimumEigensolver()
result = npme.compute_minimum_eigenvalue(qubit_op)
x = sample_most_likely(result.eigenstate)
print(x)
print(f'Objective value computed by the NumPyMinimumEigensolver is {objective_value(x, w)}')
[1 1 0 0]
Objective value computed by the NumPyMinimumEigensolver is 3
It is also possible to use VQE as is shown below
[8]:
from qiskit.algorithms import VQE
from qiskit.circuit.library import TwoLocal
algorithm_globals.random_seed = 10598
optimizer = COBYLA()
ansatz = TwoLocal(qubit_op.num_qubits, 'ry', 'cz', reps=5, entanglement='linear')
vqe = VQE(ansatz, optimizer, quantum_instance=Aer.get_backend('statevector_simulator'))
result = vqe.compute_minimum_eigenvalue(qubit_op)
x = sample_most_likely(result.eigenstate)
print(x)
print(f'Objective value computed by VQE is {objective_value(x, w)}')
/home/computertreker/git/qiskit/qiskit/.tox/docs/lib/python3.7/site-packages/sympy/core/expr.py:3951: SymPyDeprecationWarning:
expr_free_symbols method has been deprecated since SymPy 1.9. See
https://github.com/sympy/sympy/issues/21494 for more info.
deprecated_since_version="1.9").warn()
[0. 1. 0. 1.]
Objective value computed by VQE is 3.0
[9]:
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
/home/computertreker/git/qiskit/qiskit/.tox/docs/lib/python3.7/site-packages/qiskit/aqua/__init__.py:86: DeprecationWarning: The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>
warn_package('aqua', 'qiskit-terra')
Version Information
| Qiskit Software | Version |
|---|---|
qiskit-terra | 0.18.2 |
qiskit-aer | 0.8.2 |
qiskit-ignis | 0.6.0 |
qiskit-ibmq-provider | 0.16.0 |
qiskit-aqua | 0.9.5 |
qiskit | 0.29.1 |
qiskit-nature | 0.2.2 |
qiskit-finance | 0.3.0 |
qiskit-optimization | 0.2.3 |
qiskit-machine-learning | 0.2.1 |
| System information | |
| Python | 3.7.12 (default, Nov 22 2021, 14:57:10) [GCC 11.1.0] |
| OS | Linux |
| CPUs | 32 |
| Memory (Gb) | 125.71650314331055 |
| Tue Jan 04 11:05:00 2022 EST | |
This code is a part of Qiskit
© Copyright IBM 2017, 2022.
This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.