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Author: Emirhan BULUT
Date created: 2022/11/12
Last modified: 2022/11/12
Description: A worldwide study has been conducted on the emission values of SO2 gases released from diesel engines in the world (class 1 if it has increased compared to the previous year, class 0 if there has been a decrease compared to the previous year, and class 0 for the starting years). In this research, 5G compatible quantum algorithms were designed by me. Quantum computer was used for the process. The minimum number of qubits is set for use on all computers. Finally, the same data was tested in the classical deep neural network (deep learning) network and Machine Learning algorithm (Random Forest). On the basis of test accuracy, the quantum5 algorithm was found to be more performant than all of them.
The aim of the project is to design a Quantum Artificial Intelligence brain that learns the emission values of SO2 gases released from diesel engines in the world with the 8-layer Quantum Algorithm, and then to compare the performance of the created Quantum Artificial Intelligence brain with Machine Learning, Deep Learning (Classical Neuronal Networks).
Quantum Artificial Intelligence algorithms have been proven to be more performant than Machine Learning and Deep Learning artificial intelligence systems.
Quantum AI algorithms have been proven to be faster than Machine Learning and Deep Learning artificial intelligence systems.
It has been proven that Quantum Artificial Intelligence algorithms use less energy in commercial/academic uses after model formation than Machine Learning and Deep Learning artificial intelligence systems. You can access this proof by file size.
This project has proven to be compatible with 5G technology. The result obtained from the logarithm of the division of the byte rates transferred at 5G speed to the model accuracy is more than the result obtained in 4G technology, as in the following mathematical calculation.
5G = 10Gbps
4G = 0.1Gbps
log(5G/model_byte) = 2.698970004336X
log(4G/model_byte) = 0.69897000433602X
I proudly present this software,
Thank you.
Emirhan BULUT
 Run in Google Colab
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 View source on GitHub
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!pip install pip install tensorflow==2.7.0
!pip install pennylane!pip install pennylane-lightning[gpu]!pip install pennylane-lightning[qubits]import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow import keras
from keras.models import Model
from tensorflow.keras import layers
import numpy as np
import numpy
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import nltk
import re
import pennylane as qml
from pennylane import numpy as p_np
from pennylane.templates.state_preparations import MottonenStatePreparation
from pennylane.templates.layers import StronglyEntanglingLayers
from nltk.corpus import stopwords
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import tensorflow as tf!git clone https://github.com/emirhanai/SO2-Emission-Prediction-from-Diesel-Engines-with-Quantum-Technology-5G-Cloning into 'SO2-Emission-Prediction-from-Diesel-Engines-with-Quantum-Technology-5G-'...
remote: Enumerating objects: 10, done.�[K
remote: Counting objects: 100% (10/10), done.�[K
remote: Compressing objects: 100% (10/10), done.�[K
remote: Total 10 (delta 1), reused 0 (delta 0), pack-reused 0�[K
Unpacking objects: 100% (10/10), done.
cd /content/SO2-Emission-Prediction-from-Diesel-Engines-with-Quantum-Technology-5G-/content/SO2-Emission-Prediction-from-Diesel-Engines-with-Quantum-Technology-5G-
X_train,X_test,y_train,y_test = numpy.load("X_train.npy"),numpy.load("X_test.npy"),numpy.load("y_train.npy"),numpy.load("y_test.npy")cd /content//content
n_qubits = 2
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.AngleEmbedding(inputs, wires=range(n_qubits))
qml.BasicEntanglerLayers(weights, wires=range(n_qubits))
return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]
n_layers = 8
weight_shapes = {"weights": (n_layers, n_qubits)}clayer_1 = tf.keras.layers.Dense(1, activation="relu")
# construct the model
inputs = tf.keras.Input(shape=(1,))
x = clayer_1(inputs)
x = tf.keras.layers.Dropout(0.1)(x)
x = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=1)(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
opt = tf.keras.optimizers.SGD(learning_rate=0.2)
model.compile(opt, loss="mse", metrics=["accuracy"])model_fit = model.fit(X_train, y_train, epochs=2, batch_size=256,shuffle=True, validation_data=(X_test,y_test), verbose=2,callbacks=[keras.callbacks.ModelCheckpoint("/content/model/model_{epoch}.h5")])Epoch 1/2
1/1 - 10s - loss: 0.6494 - accuracy: 0.7473 - val_loss: 0.3424 - val_accuracy: 0.7273 - 10s/epoch - 10s/step
Epoch 2/2
1/1 - 10s - loss: 0.3125 - accuracy: 0.7473 - val_loss: 0.2480 - val_accuracy: 0.8182 - 10s/epoch - 10s/step
Model Evaluate for Quantum
model.evaluate(X_test,y_test)1/1 [==============================] - 1s 782ms/step - loss: 0.2480 - accuracy: 0.8182
[0.24798709154129028, 0.8181818127632141]
##Model Summary and Chart for Quantum
Model Summary for Quantum
model.summary()Model: "model_48"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_49 (InputLayer) [(None, 1)] 0
dense_50 (Dense) (None, 1) 2
dropout_48 (Dropout) (None, 1) 0
keras_layer_52 (KerasLayer) (None, 1) 16
=================================================================
Total params: 18
Trainable params: 18
Non-trainable params: 0
_________________________________________________________________
from sklearn.ensemble import RandomForestClassifier
ml = RandomForestClassifier()
ml.fit(X_train, y_train)
ml.score(X_test,y_test)0.6363636363636364
classic1 = tf.keras.layers.Dense(4,activation="relu")
classic2 = tf.keras.layers.Dense(3,activation="relu")
classic6 = tf.keras.layers.Dense(1, activation="sigmoid")
# construct the model
classic_inputs = tf.keras.Input(shape=(1,))
classic_ai = classic1(classic_inputs)
classic_ai = tf.keras.layers.Dropout(0.1)(classic_ai)
csai1, csai2, csai3, csai4 = tf.split(classic_ai, 4, axis=1)
csai1 = classic2(csai1)
classic_ai = tf.concat([csai1], axis=1)
classic_outputs = classic6(classic_ai)
classic_ai_model = tf.keras.Model(inputs=classic_inputs, outputs=classic_outputs)
classic_opt = tf.keras.optimizers.SGD(learning_rate=0.2)
classic_ai_model.compile(classic_opt, loss="mse", metrics=["accuracy"])classic_fit = classic_ai_model.fit(X_train, y_train, epochs=2, batch_size=256, validation_data=(X_test,y_test), verbose=2,shuffle=True)Epoch 1/2
WARNING:tensorflow:6 out of the last 15 calls to <function Model.make_test_function.<locals>.test_function at 0x7fc8abc53d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
1/1 - 0s - loss: 0.2960 - accuracy: 0.2527 - val_loss: 0.2929 - val_accuracy: 0.1818 - 475ms/epoch - 475ms/step
Epoch 2/2
1/1 - 0s - loss: 0.2842 - accuracy: 0.2967 - val_loss: 0.2804 - val_accuracy: 0.1818 - 24ms/epoch - 24ms/step
classic_ai_model.evaluate(X_test,y_test)1/1 [==============================] - 0s 20ms/step - loss: 0.2423 - accuracy: 0.7273
[0.24225696921348572, 0.7272727489471436]
classic_ai_model.summary()Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 1)] 0
dense_1 (Dense) (None, 4) 8
dropout_1 (Dropout) (None, 4) 0
tf.split (TFOpLambda) [(None, 1), 0
(None, 1),
(None, 1),
(None, 1)]
dense_2 (Dense) (None, 3) 6
tf.identity (TFOpLambda) (None, 3) 0
dense_3 (Dense) (None, 1) 4
=================================================================
Total params: 18
Trainable params: 18
Non-trainable params: 0
_________________________________________________________________
plt.figure(figsize=(10,5))
plt.plot(classic_fit.history['accuracy'], label='Classical Neural Network Acc')
plt.plot(classic_fit.history['val_accuracy'], label='Classical Neural Network Val Acc')
plt.plot(model_fit.history['accuracy'], label='MyQuantum 5G Algorithm Acc')
plt.plot(model_fit.history['val_accuracy'], label='MyQuantum 5G Algorithm Val Acc')
plt.title("Deep Learning (Classic Neural Network) vs MyQuantum 5G Algorithm \n Emirhan BULUT")
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()<matplotlib.legend.Legend at 0x7fc930321d90>
_28_1.png)
plt.figure(figsize=(10,5))
plt.plot(classic_fit.history['accuracy'], label='Deep Learning Acc')
plt.plot(classic_fit.history['val_accuracy'], label='Deep Learning Val Acc')
plt.plot(model_fit.history['accuracy'], label='MyQuantum 5G Algorithm Acc')
plt.plot(model_fit.history['val_accuracy'], label='MyQuantum 5G Algorithm Val Acc')
plt.title("Deep Learning (Classic Neural Network) vs MyQuantum 5G Algorithm \n Emirhan BULUT")
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()<matplotlib.legend.Legend at 0x7fc8abe21f10>
_29_1.png)
plt.figure(figsize=(10,5))
plt.plot(model_fit.history['accuracy'], label='MyQuantum 5G Algorithm Acc')
plt.plot(model_fit.history['val_accuracy'], label='MyQuantum 5G Algorithm Val Acc')
plt.plot([ml.score(X_train,y_train),ml.score(X_train,y_train)], label='ML Algorithm (RandomForest) Acc')
plt.plot([ml.score(X_test,y_test),ml.score(X_test,y_test)], label='ML Algorithm (RandomForest) Val Acc')
plt.title("ML Algorithm (RandomForest) vs MyQuantum 5G Algorithm \n Emirhan BULUT")
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()<matplotlib.legend.Legend at 0x7fc8d440dd10>
_31_1.png)
#Classic AI model save
classic_ai_model.save("classicai.h5")
#Quantum 5 AI model save
model.save("quantum5g.h5")import os
classic = "classicai.h5"
quantum = "quantum5g.h5"
file_stats_classic = os.stat(classic)
file_stats_quantum = os.stat(quantum)
print(f'Classic AI Model Size in Bytes is {file_stats_classic.st_size}')
print(f'Quantum 5G Model Size in Bytes is {file_stats_quantum.st_size}')Classic AI Model Size in Bytes is 24120
Quantum 5G Model Size in Bytes is 20384
plt.figure(figsize=(10,5))
plt.plot([file_stats_classic.st_size,file_stats_classic.st_size], label='Classic AI Model Size in Bytes')
plt.plot([file_stats_quantum.st_size,file_stats_quantum.st_size], label='Quantum 5G Model Size in Bytes')
plt.title("Classical AI Model Bytes vs Quantum 5G AI Model Bytes \n Emirhan BULUT")
plt.xlabel('0-1 range')
plt.ylabel('Bytes')
plt.legend()<matplotlib.legend.Legend at 0x7fc8ac152dd0>
_35_1.png)