# 2. 6-Machine Learning (QBoost) with Quantum Annealing# In this section, we describe an aplication of machine lerning (ML) with quantum annealing (QA) optimization.

In the first, we show app;ication for clustering task using JijModeling and OpenJij.
In the seconde, we execute an ensamble study called QBoost with PyQUBO and D-Wave sampler.

## 2.1. Clustering#

Clustering is the task of deviding given set of data into $n$ clusters ($n$ is our input). For the sake of simplicity, let us consider the number of cluster $n$ is 2 in this time.

### 2.1.1. Clustering Hamiltonian#

We demonstrate clustering by minimizing the following Hamiltonians.

$H = - \sum_{i, j} \frac{1}{2}d_{i,j} (1 - \sigma _i \sigma_j)$

Where $i, j$ is sample No., $d_{i, j}$ is a distance between $i$ and $j$, $\sigma_i=\{-1,1\}$ is spin variable that indicates whether $i$ belong to one of the two clusters.

Each term of this Hamiltonian sum behaves as follows.

• 0 for $\sigma_i = \sigma_j$

• $d_{i,j}$ for $\sigma_i \neq \sigma_j$

Note that minus of R.H.S., Hamiltonian means the problem is “Choosing pairs of $\{\sigma _1, \sigma _2 \ldots \}$ that maximizes the distance between the samples of different classes”.

### 2.1.2. Importing the required libraries#

We import several libraries for clustring.

# import libraries
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
from scipy.spatial import distance_matrix

import openjij as oj
import jijmodeling as jm
from jijmodeling.transpiler.pyqubo.to_pyqubo import to_pyqubo


### 2.1.3. Clustering with JijModeling and OpenJij#

At first, we formulate the mathmatical model in JijModeling. We cannot use spin variable in Jijmodeling, so we change spin variable $\sigma_i$ to binary variable $x_i$ by using the relationship $\sigma_i = 2x_i - 1$.

problem = jm.Problem("clustering")
d = jm.Placeholder("d", dim=2)
N = d.shape
x = jm.Binary("x", shape=(N))
i = jm.Element("i", (0, N))
j = jm.Element("j", (0, N))
problem += (
-1 / 2 * jm.Sum([i, j], d[i, j] * (1 - (2 * x[i] - 1) * (2 * x[j] - 1)))
)
problem

\begin{alignat*}{4}\text{Problem} & \text{: clustering} \\\min & \quad \left( -0.5 \right) \cdot \sum_{ i = 0 }^{ d_{\mathrm{shape}(0)} - 1 } \sum_{ j = 0 }^{ d_{\mathrm{shape}(0)} - 1 } d_{i,j} \cdot \left( 1 - \left( 2 \cdot x_{i} - 1 \right) \cdot \left( 2 \cdot x_{j} - 1 \right) \right) \\& x_{i_{0}} \in \{0, 1\}\end{alignat*}

### 2.1.4. Make artificial data#

Next, we create instance data for the clustering problem.

In this case, let us generate linearly separable data in a two-dimensional plane artificially. Our clustering algortihm is unsupervised learning algorithm, so we do not need to prepare answer data.

data = []
label = []
N = 100
for i in range(N):
# generate 0 to 1 random number
p = np.random.uniform(0, 1)
# set class 1 when certain condition are met, and -1 when it are not met
cls = 1 if p > 0.5 else -1
# create random numbers following a normal distribution
data.append(np.random.normal(0, 0.5, 2) + np.array([cls, cls]))
label.append(cls)
# formatted as a DataFrame
df1 = pd.DataFrame(data, columns=["x", "y"], index=range(len(data)))
df1["label"] = label


Let us see the data of the clustering problem.

# visualize dataset
df1.plot(kind="scatter", x="x", y="y")
plt.show() instance_data = {"d": distance_matrix(df1, df1)}


### 2.1.5. Colsving the clustering problem by using OpenJij#

We create mathmatical model and instance data, so let us solve the clustering problem by using openjij.

pyq_obj, pyq_cache = to_pyqubo(problem, instance_data, {})
qubo, constant = pyq_obj.compile().to_qubo()
sampler = oj.SASampler()
response = sampler.sample_qubo(qubo)
result = pyq_cache.decode(response)

for idx in range(0, N):
if idx in result.record.solution["x"]:
plt.scatter(df1.loc[idx]["x"], df1.loc[idx]["y"], color="b")
else:
plt.scatter(df1.loc[idx]["x"], df1.loc[idx]["y"], color="r") We can see the data is clearly sperated by blue and red class.

## 2.2. QBoost#

QBoost is a one of the ensamble learning using QA. Ensamble learning involves preparing a number of weak predictors and combining the results of each of these predictors to obtain the final prediction result.

QBoost uses QA to optimize the best combination of learners for a given training data. We handle classification problem in this time.

We define that the set of $D$ training data are $\{\vec x^{(d)}\}(d=1, ..., D)$, corresponding label are $\{y^{(d)}\}(d=1, ..., D), y^{(d)}\in \{-1, 1\}$ and the (function) set of $N$ weak learner is $\{C_i\}(i=1, ..., N)$. For some data $\vec x^{(d)}$, $C_i(\vec x^{(d)})\in \{-1, 1\}$.

Based on the definitions above, the classification labels are as follows.

${\rm sgn}\left( \sum_{i=1}^{N} w_i C_i({\vec x}^{(d)})\right)$

Where $w_i\in\{0, 1\} (i=1, ..., N)$, is a weight of each predictor (bool value to adopt or not adopt the predictor for the final prediction).QBoost optimizes the combination of $w_i$ so that prediction matches the training data while erasing the number of weak learners.

Hamiltonian in this problem is as follows.

$H(\vec w) = \sum_{d=1}^{D} \left( \frac{1}{N}\sum_{i=1}^{N} w_i C_i(\vec x^{(d)})-y^{(d)} \right)^2 + \lambda \sum _i^N w_i$

The first term represents the difference between weak classifier and the correct label. The second term represents a degree of the number of weak classifier to be employed in the final classifier. $\lambda$ is the regularization parameter that adjust how much the number of weak classifiers affects the total Hamiltonian.

We optimize this Hamiltonian by recognizing the first term as a cost (objective function) and the second term as a constraint.Minimizing with QA allows us to obtain a combination of weak classifiers that best fits the training data.

### 2.2.1. Preparation of dataset#

Let us try QBoost. We use the cancer identification dataset from scikit-learn for training data. For simplicity, we will only use two character types for training: “0” and “1”.

# import libraries
import pandas as pd
from scipy import stats
from sklearn import datasets
from sklearn import metrics

# load data
# set the number of training data & test data
num_train = 450


In this time, we consider that feature of noise exists.

data_noisy = np.concatenate(
(cancerdata.data, np.random.rand(cancerdata.data.shape, 30)), axis=1
)
print(data_noisy.shape)

(569, 60)

# convert from label {0, 1} to {-1, 1}
labels = (cancerdata.target - 0.5) * 2

# divide dataset to training and test
X_train = data_noisy[:num_train, :]
X_test = data_noisy[num_train:, :]
y_train = labels[:num_train]
y_test = labels[num_train:]

# from the result of weak learnor
def aggre_mean(Y_list):
return ((np.mean(Y_list, axis=0) > 0) - 0.5) * 2


### 2.2.2. Creating Set of Weak Learner#

We make weak learner with scikit-learn. In this time, we choose decision stump. Desision stump is a single-layer decision tree. As it will be used as a weak classifier, the features to be used for segmentation are selected randomly (it’s a good understanding that we execute single-layer of random forest).

# import required libraries
from sklearn.tree import DecisionTreeClassifier as DTC

# set the number of weak classifier
num_clf = 32
# set the number of ensembles to be taken out for one sample in bootstrap sampling
sample_train = 40
# set model
models = [DTC(splitter="random", max_depth=1) for i in range(num_clf)]
for model in models:
# extract randomly
train_idx = np.random.choice(np.arange(X_train.shape), sample_train)
# make decision tree with variables
model.fit(X=X_train[train_idx], y=y_train[train_idx])
y_pred_list_train = []
for model in models:
# execute prediction with model
y_pred_list_train.append(model.predict(X_train))
y_pred_list_train = np.asanyarray(y_pred_list_train)
y_pred_train = np.sign(y_pred_list_train)


We look accuracy of all weak learner as the final classifier. Henceforth, we refer to this combination as baseline.

y_pred_list_test = []
for model in models:
# execute with test data
y_pred_list_test.append(model.predict(X_test))

y_pred_list_test = np.array(y_pred_list_test)
y_pred_test = np.sign(np.sum(y_pred_list_test, axis=0))
# compute score of prediction accuracy
acc_test_base = metrics.accuracy_score(y_true=y_test, y_pred=y_pred_test)
print(acc_test_base)

0.9411764705882353


### 2.2.3. Execute QBoost with OpenJij#

Let us creater QBoost model.

# set class of QBoost
class QBoost:
def __init__(self, y_train, ys_pred):
self.instance_data = {"y": y_train, "C": ys_pred}
self.qboost_Hamiltonian()
self.pyq_obj, self.pyq_cache = to_pyqubo(
self.problem, self.instance_data, {}
)

def qboost_Hamiltonian(self):
problem = jm.Problem("QBoost")
C = jm.Placeholder("C", dim=2)
y = jm.Placeholder("y", dim=1)
N = C.shape.set_latex("N")
D = C.shape.set_latex("D")
w = jm.Binary("w", shape=(N))
i = jm.Element("i", (0, N))
d = jm.Element("d", (0, D))
obj = jm.Sum(
d,
(1 / N * jm.Sum(i, w[i] * C[i, d]) - y[d])
* (1 / N * jm.Sum(i, w[i] * C[i, d]) - y[d]),
)
constraint = jm.Constraint("constraint", jm.Sum(i, w[i]))
problem += obj
problem += constraint
self.problem = problem

def sampling(self, qubo, **kwargs):
sampler = oj.SASampler(**kwargs)
response = sampler.sample_qubo(qubo)
return response

def decode(self, response):
return self.pyq_cache.decode(response)

# set function for converting to QUBO
def to_qubo(self, norm_param=1):
# set value of hyperparameter
self.multiplier = {"constraint": norm_param}
model = self.pyq_obj.compile()
return model.to_qubo(feed_dict=self.multiplier)

qboost = QBoost(y_train=y_train, ys_pred=y_pred_list_train)
qboost.problem

\begin{alignat*}{4}\text{Problem} & \text{: QBoost} \\\min & \quad \sum_{ d = 0 }^{ D - 1 } \left( \frac{ 1 }{ N } \cdot \sum_{ i = 0 }^{ N - 1 } w_{i} \cdot C_{i,d} - y_{d} \right) \cdot \left( \frac{ 1 }{ N } \cdot \sum_{ i = 0 }^{ N - 1 } w_{i} \cdot C_{i,d} - y_{d} \right) \\\text{s.t.} & \\& \text{constraint} :\\ &\quad \quad \sum_{ i = 0 }^{ N - 1 } w_{i} = 0,\\[8pt]& w_{i_{0}} \in \{0, 1\}\end{alignat*}
# make QUBO with lambda=3
qubo = qboost.to_qubo(1)
response = qboost.sampling(qubo, num_reads=100, num_sweeps=10)
result = qboost.decode(response)


Let us check the accuracy in the training/validation data when using a combination of weak classifiers obtained by D-Wave.

accs_train_Dwaves = []
accs_test_Dwaves = []

for solution in result.record.solution["w"]:
idx_clf_DWave = solution
y_pred_train_DWave = np.sign(
np.sum(y_pred_list_train[idx_clf_DWave, :], axis=0)
)
y_pred_test_DWave = np.sign(
np.sum(y_pred_list_test[idx_clf_DWave, :], axis=0)
)
acc_train_DWave = metrics.accuracy_score(
y_true=y_train, y_pred=y_pred_train_DWave
)
acc_test_DWave = metrics.accuracy_score(
y_true=y_test, y_pred=y_pred_test_DWave
)
accs_train_Dwaves.append(acc_train_DWave)
accs_test_Dwaves.append(acc_test_DWave)

energies = result.evaluation.energy


We make a graph with energy on the horizontal axis and accuracy on the vertical axis.

plt.figure(figsize=(12, 8))
plt.scatter(energies, accs_train_Dwaves, label="train")
plt.scatter(energies, accs_test_Dwaves, label="test")
plt.xlabel("energy")
plt.ylabel("accuracy")
plt.title("relationship between energy and accuracy")
plt.grid()
plt.legend()
plt.show() print("base accuracy is {}".format(acc_test_base))
print("max accuracy of QBoost is {}".format(max(accs_test_Dwaves)))
print(
"average accuracy of QBoost is {}".format(
np.mean(np.asarray(accs_test_Dwaves))
)
)

base accuracy is 0.9411764705882353
max accuracy of QBoost is 0.9495798319327731
average accuracy of QBoost is 0.9284033613445378