注釈
このページは tutorials/optimization/8_cvar_optimization.ipynb から生成されました。
IBM Quantum lab でインタラクティブに実行します。
CVaRを使用した変分量子最適化の改善¶
はじめに¶
このノートブックは、Qiskitが提供する変分量子最適化アルゴリズム内で [1] で紹介されたConditional Value at Risk（CVaR）目的関数を使用する方法を示しています。 特に、 VQE を使用して MinimumEigenOptimizer を設定する方法を示します。 考慮される最適化問題に対応する客観的価値を持つショットの特定のセットについて、信頼水準 \(\alpha \in [0, 1]\) のCVaRは、 \(\alpha\) ベストショットの平均として定義されます。 したがって、 \(\alpha = 1\) は標準の期待値に対応し、 \(\alpha=0\) は与えられたショットの最小値に対応し、 \(\alpha \in (0, 1)\) はより良いショットに焦点を合わせる間のトレードオフですが、最適化ランドスケープをスムーズにするために平均化を適用します。
参考文献¶
[1] P. Barkoutsos et al., Improving Variational Quantum Optimization using CVaR, Quantum 4, 256 (2020).
[1]:
from qiskit.circuit.library import RealAmplitudes
from qiskit.aqua.components.optimizers import COBYLA
from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
from qiskit.aqua.operators import PauliExpectation, CVaRExpectation
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.converters import LinearEqualityToPenalty
from qiskit.optimization.algorithms import MinimumEigenOptimizer
from qiskit import execute, Aer
from qiskit.aqua import aqua_globals
import numpy as np
import matplotlib.pyplot as plt
from docplex.mp.model import Model
[2]:
aqua_globals.random_seed = 123456
ポートフォリオの最適化¶
以下では、 [1] で紹介したポートフォリオ最適化の問題インスタンスを定義します。
[3]:
# prepare problem instance
n = 6 # number of assets
q = 0.5 # risk factor
budget = n // 2 # budget
penalty = 2*n # scaling of penalty term
[4]:
# instance from [1]
mu = np.array([0.7313, 0.9893, 0.2725, 0.8750, 0.7667, 0.3622])
sigma = np.array([
[ 0.7312, -0.6233, 0.4689, -0.5452, -0.0082, -0.3809],
[-0.6233, 2.4732, -0.7538, 2.4659, -0.0733, 0.8945],
[ 0.4689, -0.7538, 1.1543, -1.4095, 0.0007, -0.4301],
[-0.5452, 2.4659, -1.4095, 3.5067, 0.2012, 1.0922],
[-0.0082, -0.0733, 0.0007, 0.2012, 0.6231, 0.1509],
[-0.3809, 0.8945, -0.4301, 1.0922, 0.1509, 0.8992]
])
# or create random instance
# mu, sigma = portfolio.random_model(n, seed=123) # expected returns and covariance matrix
[5]:
# create docplex model
mdl = Model('portfolio_optimization')
x = mdl.binary_var_list('x{}'.format(i) for i in range(n))
objective = mdl.sum([mu[i]*x[i] for i in range(n)])
objective -= q * mdl.sum([sigma[i,j]*x[i]*x[j] for i in range(n) for j in range(n)])
mdl.maximize(objective)
mdl.add_constraint(mdl.sum(x[i] for i in range(n)) == budget)
# case to
qp = QuadraticProgram()
qp.from_docplex(mdl)
[6]:
# solve classically as reference
opt_result = MinimumEigenOptimizer(NumPyMinimumEigensolver()).solve(qp)
opt_result
[6]:
optimal function value: 1.27835
optimal value: [1. 1. 0. 0. 1. 0.]
status: SUCCESS
[7]:
# we convert the problem to an unconstrained problem for further analysis,
# otherwise this would not be necessary as the MinimumEigenSolver would do this
# translation automatically
linear2penalty = LinearEqualityToPenalty(penalty=penalty)
qp = linear2penalty.convert(qp)
_, offset = qp.to_ising()
VQEを使用した最小固有オプティマイザー（Minimum Eigen Optimizer）¶
[8]:
# set classical optimizer
maxiter = 100
optimizer = COBYLA(maxiter=maxiter)
# set variational ansatz
var_form = RealAmplitudes(n, reps=1)
m = var_form.num_parameters
# set backend
backend_name = 'qasm_simulator' # use this for QASM simulator
# backend_name = 'statevector_simulator' # use this for statevector simlator
backend = Aer.get_backend(backend_name)
# run variational optimization for different values of alpha
alphas = [1.0, 0.50, 0.25] # confidence levels to be evaluated
[9]:
# dictionaries to store optimization progress and results
objectives = {alpha: [] for alpha in alphas} # set of tested objective functions w.r.t. alpha
results = {} # results of minimum eigensolver w.r.t alpha
# callback to store intermediate results
def callback(i, params, obj, stddev, alpha):
# we translate the objective from the internal Ising representation
# to the original optimization problem
objectives[alpha] += [-(obj + offset)]
# loop over all given alpha values
for alpha in alphas:
# initialize CVaR_alpha objective
cvar_exp = CVaRExpectation(alpha, PauliExpectation())
cvar_exp.compute_variance = lambda x: [0] # to be fixed in PR #1373
# initialize VQE using CVaR
vqe = VQE(expectation=cvar_exp, optimizer=optimizer, var_form=var_form, quantum_instance=backend,
callback=lambda i, params, obj, stddev: callback(i, params, obj, stddev, alpha))
# initialize optimization algorithm based on CVaR-VQE
opt_alg = MinimumEigenOptimizer(vqe)
# solve problem
results[alpha] = opt_alg.solve(qp)
# print results
print('alpha = {}:'.format(alpha))
print(results[alpha])
print()
alpha = 1.0:
optimal function value: 0.7295999999999907
optimal value: [0. 1. 1. 0. 1. 0.]
status: SUCCESS
alpha = 0.5:
optimal function value: 0.7295999999999907
optimal value: [0. 1. 1. 0. 1. 0.]
status: SUCCESS
alpha = 0.25:
optimal function value: 1.2783500000000068
optimal value: [1. 1. 0. 0. 1. 0.]
status: SUCCESS
[10]:
# plot resulting history of objective values
plt.figure(figsize=(10, 5))
plt.plot([0, maxiter], [opt_result.fval, opt_result.fval], 'r--', linewidth=2, label='optimum')
for alpha in alphas:
plt.plot(objectives[alpha], label='alpha = %.2f' % alpha, linewidth=2)
plt.legend(loc='lower right', fontsize=14)
plt.xlim(0, maxiter)
plt.xticks(fontsize=14)
plt.xlabel('iterations', fontsize=14)
plt.yticks(fontsize=14)
plt.ylabel('objective value', fontsize=14)
plt.show()
[11]:
# evaluate and sort all objective values
objective_values = np.zeros(2**n)
for i in range(2**n):
x_bin = ('{0:0%sb}' % n).format(i)
x = [0 if x_ == '0' else 1 for x_ in reversed(x_bin)]
objective_values[i] = qp.objective.evaluate(x)
ind = np.argsort(objective_values)
# evaluate final optimal probability for each alpha
probabilities = np.zeros(len(objective_values))
for alpha in alphas:
if backend_name == 'qasm_simulator':
counts = results[alpha].min_eigen_solver_result.eigenstate
shots = sum(counts.values())
for key, val in counts.items():
i = int(key, 2)
probabilities[i] = val / shots
else:
probabilities = np.abs(results[alpha].min_eigen_solver_result.eigenstate)**2
print('optimal probabilitiy (alpha = %.2f): %.4f' % (alpha, probabilities[ind][-1:]))
optimal probabilitiy (alpha = 1.00): 0.0000
optimal probabilitiy (alpha = 0.50): 0.0098
optimal probabilitiy (alpha = 0.25): 0.2402
[12]:
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
Version Information
| Qiskit Software | Version |
|---|---|
| Qiskit | 0.23.0 |
| Terra | 0.16.0 |
| Aer | 0.7.0 |
| Ignis | 0.5.0 |
| Aqua | 0.8.0 |
| IBM Q Provider | 0.11.0 |
| System information | |
| Python | 3.7.4 (default, Aug 13 2019, 15:17:50) [Clang 4.0.1 (tags/RELEASE_401/final)] |
| OS | Darwin |
| CPUs | 6 |
| Memory (Gb) | 16.0 |
| Mon Oct 19 23:44:24 2020 CEST | |
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