Comparing Classical and Quantum Algorithms in Analyzing NASA Kepler Mission Data

Humanity has long sought precise and scientific answers to its endless questions about the universe.One of the greatest challenges in this pursuit is the discovery of extraterrestrial life and Earth like planets.The Kepler mission, launched in 2009, marked a major milestone in this direction.Its primary objective was to discover exoplanets and assess their habitability.With the vast amount of data transmitted by the Kepler telescope, scientists faced a new challenge: analyzing and extracting meaningful insights from this data.Initially, classical computational algorithms such as regression, k-nearest neighbor, decision trees, random forests, and various types ofneural networks proved effective for this task.However, as the volume of data grew, the limitations of these methods became evident.Questions arose regarding the ability of these algorithms to detect complex patterns and the computational time required for extensive calculations, prompting researchers to explore alternative solutions.Quantum computing, leveraging the principles of quantum mechanics such as superposition and entanglement, exponentially enhances the ability to perform complex computations.Unlike classical bits, qubits allow quantum computers to process multiple calculations simultaneously, making them particularly valuable for analyzing large and intricate datasets.Among the quantum algorithms currently in use, notable examples include quantum support vector machines, quantum neural networks, quantum k-nearest neighbor, and variational algorithms.Currently, both classical and quantum algorithms have distinct advantages and limitations.Classical algorithms are widely applied to everyday computational problems and are relatively easy to implement.In contrast, quantum algorithms hold the potential to solve complex problems that are either infeasible or extremely time-consuming for classical computers.The ongoing competition between these two paradigms has motivated us to compare their efficiency, aiming to assess their current capabilities and future potential.