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Quantum Computing: Principles, Algorithms, and Future Computing Potential

Nirmala Priyadharshini P, Mr. Mohammed Ansar Ali M A, Mrs. Rajaprabha, Mr. K.Bhagavathi Raja

Published Jan 19, 2026• 0 citations
Publisher
DOI
10.5281/zenodo.18696085

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Quantum computing, based on the fundamental principles of quantum mechanics, has become a breakthrough that has the potential to revolutionize computing. At the forefront of this quantum revolution are quantum algorithms that use the origin of qubits to solve computational problems much faster than classical algorithms. This article provides a review of quantum algorithms to introduce their principles, examine famous examples such as the Grover and Shor algorithms, and delve into their applications, including quantum machine learning. Exploring the fundamentals of quantum computing begins with an overview that highlights the unique features of quantum computing compared to classical computing. We then examine several different algorithmic paradigms to highlight the unique advantages that quantum parallelism and interference bring to computing. The best knowledge of quantum algorithms focuses on Grover's blind search, Shor's efficient factorization algorithm, and the quantum phase approach for solving quantum chemistry and eigenvalue problems. As well as celebrating the achievements of quantum algorithms, this research also looks at the challenges and limitations that hinder their widespread use. Topics such as decoherence, error correction, and finding errors in quantum computing are discussed in the context of overcoming obstacles to the use of practical quantum algorithms. In addition, the latest developments are clarified by showing the successes of the experiments and their implications for the future. As we stand at the forefront of a new era in computing, this article not only provides a snapshot of the current state of quantum algorithms but also speculates on future directions. The potential applications of quantum algorithms in many fields and the ongoing quest to overcome current limitations offer exciting opportunities for further research. This research aims to contribute to the ongoing debate about quantum algorithms, gaining a deeper understanding of their impact on the future of computing.
As Artificial Intelligence (AI) continues to revolutionize various industries, the integration of quantum computing promises to propel AI into unprecedented realms of capability and efficiency. This chapter explores the transformative potential of quantum computing in the future of AI, presenting a detailed overview of current advancements and future prospects. We begin by elucidating the fundamental principles of quantum computing, highlighting how quantum bits (qubits) and quantum entanglement differ fundamentally from classical computing paradigms. The discussion then moves to the synergistic relationship between AI and quantum computing, showcasing how quantum algorithms can enhance machine learning models, optimize complex problem-solving tasks, and enable real-time data processing at previously unattainable speeds. The chapter also delves into the potential of quantum computing in enhancing AI through quantum algorithms such as Grover's Algorithm and Shor’s Algorithm. Simulation results for different cases have been studied, demonstrating the effectiveness of quantum computing in AI.