Theoretical Issues in Practical Implementation of Quantum Computation

Dattatray Raghunath Kale

Quantum computing has the potential to transform a number of industries, including machine learning and optimization. This work investigates the relationship between quantum computing and machine learning, with particular attention on the creation, use, and applications of quantum machine learning algorithms for optimization issues. We present a thorough analysis of the theoretical foundation of quantum optimization algorithms, talk about how they are practically implemented on quantum computing platforms, and investigate real-world applications in a several fields. We also highlight upcoming research directions and issues in the realm of quantum machine learning.

Craig S. Lent

M. Jayalakshmi, K. Maharajan, A. Santhosh Nantha +1 more

This paper extensively studies classical and quantum computing paradigms, analysing their basic principles, architectural distinctions, computational power, and real-world applications. We introduce a formal mathematical model to define both models of computation and compare their strengths and weaknesses. Through a thorough analysis of computational complexity, we prove the theoretical superiority of quantum algorithms for certain problem domains. Moreover, recent experimental implementations of quantum computer structures are discussed here, and problems and probable development directions are marked. The study shows that, whereas quantum computing promises to be ideal for specific computationally hard issues theoretically, implementation challenges in practice remain vast. This contribution to the argument of next-generation computing architecture further illuminates prospects for future advancement in computational sciences.

Isaac L. Chuang

Kareem S. Aggour, Renee Guhde, M.K. Sommins +1 more

Quantum computing (QC) research has gained a lot of momentum recently due to several theoretical analyses that indicate that QC is significantly more efficient at solving certain classes of problems than classical computing. While experimental validation will ultimately be required, the primitive nature of current QC hardware leaves practical testing limited to trivial examples. Thus, a robust simulator is needed to study complex QC issues. Most QC simulators model ideal operations, and thus cannot predict the actual time required to execute an algorithm or quantify the effects of errors in the calculation. We have developed a novel QC simulator that models physical hardware implementations. This simulator not only allows the accurate simulation of quantum algorithms on various hardware implementations, but also takes an important step towards providing a framework to determine their true performance and vulnerability to errors.

Tzvetan S. Metodi, Arvin I. Faruque, Frederic T. Chong