Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
University of Borås, Faculty of Librarianship, Information, Education and IT.ORCID iD: 0000-0002-1539-8256
Show others and affiliations
2016 (English)In: arXiv, article id 1607.03428Article in journal (Refereed) Submitted
Abstract [en]

Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical systems, quantum control for parameter optimization is mainly pursued via gradient-based greedy algorithms. Although the quantum fitness landscape is often compatible for greedy algorithms, sometimes greedy algorithms yield poor results, especially for large-dimensional quantum systems. We employ differential evolution algorithms to circumvent the stagnation problem of non-convex optimization, and we average over the objective function to improve quantum control fidelity for noisy systems. To reduce computational cost, we introduce heuristics for early termination of runs and for adaptive selection of search subspaces. Our implementation is massively parallel and vectorized to reduce run time even further. We demonstrate our methods with two examples, namely quantum phase estimation and quantum gate design, for which we achieve superior fidelity and scalability than obtained using greedy algorithms.

Place, publisher, year, edition, pages
2016. article id 1607.03428
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:hb:diva-11638OAI: oai:DiVA.org:hb-11638DiVA, id: diva2:1062342
Available from: 2017-01-05 Created: 2017-01-05 Last updated: 2017-03-17Bibliographically approved

Open Access in DiVA

fulltext(677 kB)263 downloads
File information
File name FULLTEXT01.pdfFile size 677 kBChecksum SHA-512
094b9b3df270dd9f8687039f9bf1dcc89a51bc23d2a6b4d4469b72ef6804188bbbe2a89aa04b1d70367d6ce2df5a848024e0f229fffe4b0fb325b702f8794568
Type fulltextMimetype application/pdf

Authority records BETA

Wittek, Peter

Search in DiVA

By author/editor
Wittek, Peter
By organisation
Faculty of Librarianship, Information, Education and IT
Subatomic Physics

Search outside of DiVA

GoogleGoogle Scholar
Total: 263 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 123 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf