Repášová, Michaela (2025) Effectiveness of quantum annealing and the quantum approximate optimization algorithm for solving audio quantization problems. Master's Internship Report, Applied Mathematics.
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Abstract
Quantum computing is an emerging field of computer science and engineering. It has the potential to solve problems beyond the ability of classical computers using the unique qualities of quantum mechanics. We are in the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, where only small-scale and error-prone quantum hardware is available. Nonetheless, a wide range of algorithms and heuristics have been proposed to take advantage of the current era of quantum hardware. In this project, we study the efficacy of two such approaches, Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA), on an example of audio quantization. This use case was first studied in [1] where the authors have found that QA, using D-Wave’s 2000Q annealer, finds higher quality solutions than a comparable classical algorithm- simulated annealing. We expand on their work and compare the performance of simulated annealing, QA using D-Wave’s newer annealer Advantage, and the QAOA. Contrary to their finding, we find that simulated annealing finds higher quality solutions than QA. Moreover, we find that QAOA performs the worst.
| Item Type: | Thesis (Master's Internship Report) |
|---|---|
| Supervisor name: | Waarde, H.J. van and Camlibel, M.K. |
| Degree programme: | Applied Mathematics |
| Thesis type: | Master's Internship Report |
| Language: | English |
| Date Deposited: | 12 Aug 2025 07:55 |
| Last Modified: | 12 Aug 2025 07:55 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/36734 |
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