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PERFORMANCE OPTIMIZATION USING QUANTUM MACHINE LEARNING TECHNIQUE FOR PREDICTING FLIGHT DELAYS

Shruti S Pophale, Purushottam R. Patil, Amol Dnyaneshwar Potgantwar, Pawan Bhaladhare

Published Feb 10, 2023• 0 citations
Publisher

Abstract

Numerous companies rely on a number of different airlines to connect them with other regions of the world as a result of the increasingly important position that the aviation industry plays in today's global transportation sector. On the other hand, severe weather can have a direct impact on airline services, most notably in the form of flight delays. The solution to this problem is to accurately predict these flight delays, which enables passengers to be well prepared for the deterrent that will be caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance in order to diminish the negative impact. The goal of this work, which makes use of quantum machine learning, is to investigate the methodologies that are applied in the construction of models for forecasting flight delays that are brought on by inclement weather. In the initial phase of the project, we investigate the feasibility of employing Python-based linear Regression in conjunction with Support Vector Machine. After that, we feed the dataset into our classifier to obtain the results. In the second half of the project, our primary focus is on acquiring data, and we investigate the possibility of combining quantum machine learning with the process of gathering data by first dissecting the dataset and then determining which features are important. After looking over the results, we compared them to the outcomes of other models, such as the machine learning classifier with quantum linear regression and the quantum SVM-SVR, in order to determine which classifier would be the most effective in resolving the issue.

DOI
10.5281/zenodo.7628863

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Numerous companies rely on a number of different airlines to connect them with other regions of the world as a result of the increasingly important position that the aviation industry plays in today's global transportation sector. On the other hand, severe weather can have a direct impact on airline services, most notably in the form of flight delays. The solution to this problem is to accurately predict these flight delays, which enables passengers to be well prepared for the deterrent that will be caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance in order to diminish the negative impact. The goal of this work, which makes use of quantum machine learning, is to investigate the methodologies that are applied in the construction of models for forecasting flight delays that are brought on by inclement weather. In the initial phase of the project, we investigate the feasibility of employing Python-based linear Regression in conjunction with Support Vector Machine. After that, we feed the dataset into our classifier to obtain the results. In the second half of the project, our primary focus is on acquiring data, and we investigate the possibility of combining quantum machine learning with the process of gathering data by first dissecting the dataset and then determining which features are important. After looking over the results, we compared them to the outcomes of other models, such as the machine learning classifier with quantum linear regression and the quantum SVM-SVR, in order to determine which classifier would be the most effective in resolving the issue.
Because of the increasing emphasis put on the aviation industry in as a main industry of the global transportation industry, a number of organizations depend on different flight companies to get in touch with parts of the world which may be an effective measure to control the disease from spreading to other areas of the world. However, severe conditions can actually influence airline services in a given area of operation most significantly, flight delays. In this case, service encounters took place between the airline as a large organizational sector and the individual traveling as customers. The working for this issue is to accurately expect such flight delays though enables enhanced performers while in travel in order to be fit for destinations. Disability which will be occasioned by the existence of the aforementioned impediment. Quantum machine learning is used to examine the measures that were used in building models to analyze probability of flights. Problems associated with late and early arrival and; Delays due to unfavorable weather conditions. In the initial the process of project management, in which at the planned stage of the work, we study the feasibility of integration of Support Vector Machine with Python-based linear Regression.
Weather forecasting is a major challenge because of the unpredictable nature of atmospheric systems and the large number of interacting variables. Although traditional machine learning models have been used for weather prediction, they cannot capture the complex, nonlinear dynamics of weather. Recently, Quantum Machine Learning (QML) shows ability to improve computational speed and pattern recognition. Its practical application in weather prediction, however, is not well exploited. This work methodically analyzes QML algorithms for predicting weather states and compares their predictive performance with that of classical machine learning. We assembled extensive hourly meteorological data for Sylhet City, Bangladesh (24.9172∘N,91.8319∘24.9172^{\circ} \mathrm{N}, 91.8319^{\circ}24.9172∘N,91.8319∘E) over 185 days from Microsoft and IBM weather sites, totaling 4,504 data points. The dataset combines the main atmospheric parameters as temperature, real feel temperature, humidity, precipitation probability, visibility, dew point, and air pressure, from Microsoft and IBM weather sites. Four QML algorithms, including Quantum Random Forest (QRF), Quantum Decision Tree (QDT), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) are trained and tested for five weather state classification, and compared to their classical counterparts named SVM, Random Forest, Decision Tree, and Neural Network. All quantum experiments were run on Qiskit simulators due to current hardware limitations. Results show that classical models performed slightly better than quantum models, and the difference was not very large.
Support vector machines are powerful and frequently used machine learning methods for classification and regression tasks, which rely on the construction of kernel matrices. While crucial for the performance of this machine learning approach, choosing the most suitable kernel is highly problem-dependent. The emergence of quantum computers and quantum machine learning techniques provides new possibilities for generating powerful quantum kernels. Within this work, we solve a real-world price forecasting problem using fidelity and projected quantum kernels, which are promising candidates for the utility of near-term quantum computing. In our analysis, we examine and validate the most auspicious quantum kernels from literature and compare their performance with an optimized classical kernel. Unlike previous work on quantum support vector machines, our dataset includes categorical features that need to be encoded as numerical features, which we realize by using the one-hot-encoding scheme. One-hot-encoding, however, increases the dimensionality of the dataset significantly, which collides with the current limitations of noisy intermediate scale quantum computers. To overcome these limitations, we use autoencoders to learn a low-dimensional representation of the feature space that still maintains the most important information of the original data. To examine the impact of autoencoding, we compare the results of the encoded date with the results of the original, unencoded dataset. We could demonstrate that quantum kernels are comparable to or even better than the classical support vector machine kernels regarding the mean absolute percentage error scores for both encoded and unencoded datasets.
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.
Weather forecasting plays a critical role across various sectors, supporting strategic planning and reducing the impact of hazardous climatic conditions. However, the inherently chaotic and nonlinear nature of atmospheric systems limits the performance of conventional forecasting approaches, often resulting in prediction inaccuracies and heightened risk. This study proposes a quantum-driven predictive framework that exploits the computational strengths of Quantum Machine Learning (QML) and hybrid quantum–classical optimization to enhance the accuracy and efficiency of modern forecasting models. The framework employs Quantum Neural Networks (QNNs) combined with Variational Quantum Circuits (VQCs) to learn complex spatial–temporal dynamics embedded within large meteorological datasets. Through quantum feature encoding, key atmospheric indicators—including temperature, humidity, wind velocity, and barometric pressure—are projected into high-dimensional Hilbert spaces, enabling more expressive pattern extraction and robust predictive behavior. Simulation results reveal that the quantum-augmented approach surpasses traditional deep learning architectures in both training convergence and forecast precision when applied to extensive weather records. The hybrid design further supports scalability by intelligently distributing computational workloads between classical processors and quantum hardware. Overall, this research demonstrates the transformative potential of quantum computing in atmospheric modeling, offering a foundation for future real-time forecasting systems capable of managing the increasing complexity of global climate patterns.