Quantum Approximate Optimisation Algorithm for Protein Folding

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Perustieteiden korkeakoulu | Bachelor's thesis
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Date

2024-09-06

Department

Major/Subject

Quantum Technology

Mcode

SCI3103

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

25+4

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Abstract

Quantum Approximate Optimisation Algorithm (QAOA) has gained significant attention in the field of quantum computing. As a noisy intermediate-scale quantum (NISQ) algorithm, it has demonstrated considerable potential due to its scalability and customisability. Protein folding has persisted as one of the foremost challenges in biochemistry for the past half century. The complexity of this problem has attracted significant interest from computer scientists. The release of AlphaFold, an advanced machine learning-based model by DeepMind, provided concrete evidence that such complex problems can indeed be addressed through computational algorithms. In principle, quantum algorithms are well-suited for addressing complex problems, and numerous proof-of-principle papers addressing protein folding emerged following the advent of QAOA. This thesis extensively presents QAOA by identifying its key characteristics, as well as the construction of protein folding in terms of QAOA. The algorithm is then demonstrated and tested on small MaxCut instances.

Description

Supervisor

Raasakka, Matti

Thesis advisor

Ylinen, Lauri

Keywords

protein folding, QAOA, variational quantum algorithms, near term quantum computers, applications of quantum computing, NISQ

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https://urn.fi/URN:NBN:fi:aalto-202411197172

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[kand] Perustieteiden korkeakoulu / SCI