Applied quantum computation in the noisy intermediate-scale era for quantum machine learning: a novel approach for modern applications

Abstract

Information processing with electronic devices has been the core of the development of technology for the last 60 years, having improvements, new applications, and advances year by year. With this search for new and more advanced ways to process information, a new concept appeared 40 years ago: Quantum computing, which has ceased to be just a concept and has become a reality in recent years, opening the door to new applications, implementations, algorithms, and methods for the development of new and more advanced technologies, with Quantum information science at the core. Quantum machine learning is one of these new fields of study that investigates the interaction of concepts from quantum computation and machine learning. The paradigm of quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the com- puter as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurately, in this work, we present three imple- mentations of applied quantum machine learning: in parallel quantum computation to evaluate the speed-ups of quantum models, in cybersecurity systems for intrusion detection and the implementation of the algorithm quantum Fourier transform for quantum machine learning tasks. The results not only show how successfully quan- tum machine learning can solve current problems but also, progress on how these systems can significantly improve their performance, to achieve Quantum advantage over classical systems. All the experiments presented in this work were executed either on a Quantum simulator or real Quantum Hardware accessible from the plat- forms and frameworks from IBM Quantum, Amazon Web Services, and Xanadu Quantum Technologies Inc.