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SpringerBriefs in physics

More Problems with Quantum Computing

M. I. Dyakonov

Published Jan 1, 2020• 1 citation
Publisher
DOI
10.1007/978-3-030-42019-2_5

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•Jan 1, 2024•0 citations
Abstract Context: The rules of Quantum Mechanics have been exploited through Quantum Computing (QC) to solve specific problems and process information in expeditious ways as compared to Conventional Computing (CC) such as factoring integers. Problem: With the alluring computation capability of QC, it is still important to assess the implications and limitations of QC in solving a variety of computationally demanding problems. Method: In this regard, an empirical study was conducted to assess the efficacy of QC in terms of solving certain complex problems by keeping a tradeoff between the execution time and problem size. An analysis was performed based on the widely used Shor’s algorithms and the efficacy of QC as compared to CC was reported. Results: The outcomes show that QC has the potential to exponentially speed up the identification of a solution to certain polynomial problems that are intractable for CC. However, further research is needed to fully understand the potential and limitations of QC for Non-Polynomial (NP) complete problems.
With the rapid onset of the Quantum Computing (QC) paradigm in the field of computer science, the QC approach could enable better outcomes to solve some of the classical problems. It is plausible to speculate that quantum computers could outperform classical computers in machine learning tasks because quantum systems exhibit peculiar patterns that conventional systems are assumed to be incapable of producing. This work introduces the QC approach to Machine Learning solutions in computer science and provides a comparison of the performances of QC-based approaches against the classical ones. In addition, an attempt to discuss the potential of QC in solving classical computer science problems such as Shor’s algorithm, the Travelling Salesman problem(TSP), and the prime factorization problem is made through the empirical study of the same.In conclusion, we analyze the outcomes of our practical investigation and devise potential improvements that can be made to the QC methods employed by the solutions.
Some problems are computationally solvable but to complexity of nature of problem, they take a long time to solve. Quantum computers (QC) offer the ability to solve these problems in reasonable time using principles of quantum mechanics. One such hard problem is MaxCut, with various applications like portfolio optimization, drug discovery, and inventory management. This poster dives into solving these problems on QC with introduction to how problems are encoded mathematically (QUBO); converted into quantum language (Hamiltonian) and solved using quantum algorithm (QAOA).The goal of research was to run MaxCut problem on different variants of a quantum algorithms to analyze results.