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Description
The world is constantly challenged by complex scientific, economic, and medical problems, etc. In many cases, progress in finding solutions requires the application of modern computing machines. At the same time, the classical paradigm based on the Turing machine model has well-known limits. Some problems require computing resources that grow exponentially with the size of data, thus making solutions intractable for such applications. In the past 25 years, researchers have realized that computing machines based on the principles of quantum mechanics might offer a novel strategy for attacking these problems. In this report, we investigate how quantum and classical computers differ. To highlight these differences, we performed several experiments designed and implemented on an IBM quantum computer. In one circuit design, we investigated the measurement outcomes when a quantum system in each state, represented by the symbol |0>, is processed by a Hadamard gate. We noticed specific patterns in measurement outcomes when employing an odd and even number of Hadamard gates. For every odd number of H-gates used, we observed two distinct measurement outcomes 0,1 that were distributed with a 50% chance for each. Results suggests that a Hadamard gate is a so-called noisy gate; in the same way, flipping a coin result in two evenly distributed outcomes. If that interpretation is correct, one expects the same outcomes for an even number of gates. We resolve this paradox by positing that a quantum state can exist in a so-called superposition state, allowing for the interference of probability amplitudes.
Publication Date
Fall 11-15-2021
Language
English
Keywords
Quantum Computing; Quantum Computing and Information; Classical Computing; Modern Computers; Modern Computing VS Quantum Computing
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7000 KB
Recommended Citation
Hall, Rashad C. and Zygelman, Bernard Ph.D., "Introduction to Quantum Computing and Information" (2021). Undergraduate Research Symposium Posters. 78.
https://digitalscholarship.unlv.edu/durep_posters/78
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Comments
Faculty Mentor: Bernard Zygelman, Ph.D.