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Maskininlärning och kvantmekanik
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Machine learning and quantum mechanics (English)
Abstract [sv]

Syftet med rapporten är att redovisa för hur maskininlärning kan användas för att approximera grundtillståndsenergin av kvantsystemet som motsvaras av partikeln i en låda. Ett nätverk med radialbasfunktioner har använts med nätverket som en representation av variationsvågfunktionen. Vikterna i nätverket har uppdaterats så att väntevärdet av energin minimeras. Energiminimeringen har utförts med hjälp av variations-Monte Carlo-metoden. Lösningsmetoden som presenteras har gett en bra approximation för grundtillståndsenergin av partikeln i en låda. Metoden fungerar också när en störning i form av en linjär potential är tillagd till systemet.

Abstract [en]

This thesis aims to use machine learning to solve for the ground state energy of the quantum system corresponding to the particle in a box. A radial basis function (RBF) network is used with Gaussian functions as the variational wave function. The weights in the network are updated so that the energy expectation value is minimized, which is carried out by using the variational Monte Carlo (VMC) method. The method using machine learning succeeds in finding the ground state energy for the particle in a box. The method also works when a perturbation in the form of a linear potential is added to the infinite potential well.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:242
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255825OAI: oai:DiVA.org:kth-255825DiVA, id: diva2:1342246
Supervisors
Examiners
Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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