Protein structure prediction and design on near-term quantum computers
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| Titel: | Protein structure prediction and design on near-term quantum computers |
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| Autoren: | Linn, Hanna, 1995 |
| Quelle: | Wallenberg Centre for Quantum Technology (WACQT). |
| Schlagwörter: | variational quantum algorithms, quantum approximate optimization algorithm, hardware-efficient ansatz, protein design, quantum walk, life science, protein structure prediction, near-term intermediate-scale quantum devices, protein folding |
| Beschreibung: | In the convergence of quantum computing and life science, we explore protein structure prediction and design on near-term intermediate-scale quantum devices. We investigate the algorithmic and resource constraints of today’s quantum computers, aiming to assess their potential in solving biologically relevant problems. We describe key variational quantum algorithms, including the problem-informed Quantum Approximate Optimization Algorithm and the problem-agnostic Hardware-Efficient Ansatz. Additionally, quantum walks are examined. The computationally complex coarse-grained lattice models in protein structure prediction and design are discussed. Quantum algorithms are then applied to these models to address the utility and limitations of today’s quantum computers. The thesis critically evaluates the limitations of quantum methods in comparison to classical approaches, highlighting the trade-offs between resource requirements in today’s quantum devices and the performance of quantum algorithms. Through this interdisciplinary investigation, the work contributes to understanding how quantum algorithms may advance computational biology in today’s quantum computing landscape. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/548680 https://research.chalmers.se/publication/548680/file/548680_Fulltext.pdf |
| Datenbank: | SwePub |
Nájsť tento článok vo Web of Science | Abstract: | In the convergence of quantum computing and life science, we explore protein structure prediction and design on near-term intermediate-scale quantum devices. We investigate the algorithmic and resource constraints of today’s quantum computers, aiming to assess their potential in solving biologically relevant problems. We describe key variational quantum algorithms, including the problem-informed Quantum Approximate Optimization Algorithm and the problem-agnostic Hardware-Efficient Ansatz. Additionally, quantum walks are examined. The computationally complex coarse-grained lattice models in protein structure prediction and design are discussed. Quantum algorithms are then applied to these models to address the utility and limitations of today’s quantum computers. The thesis critically evaluates the limitations of quantum methods in comparison to classical approaches, highlighting the trade-offs between resource requirements in today’s quantum devices and the performance of quantum algorithms. Through this interdisciplinary investigation, the work contributes to understanding how quantum algorithms may advance computational biology in today’s quantum computing landscape. |
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| DOI: | 10.63959/chalmers.dt/5762 |