Jacob L. Cybulski
Founder, researcher, author and consultant at Enquanted
Also, Honorary Assoc. Prof., School of IT, Deakin
In quantum computing and quantum machine learning

• Contact:
  Email: enquiries(at)jacobcybulski.com
  Web: jacobcybulski.com
  LinkedIn: au.linkedin.com/in/jacobcybulski
  YouTube: youtube.com/ironfrown
  Twitter: @ironfrown

• Additional info:
  Selected pubs: ICCS'2024, QINP, JIT, and CHB
  Selected YouTube video: Visual Analyst 3D
  Selected YouTube training: R and RapidMiner
  Teaching approach: Experiential Learning

• Related links:
  Project: Action in Quantum Time
  Project: Aristoberry Gallery
  Profile: Google Scholar
  Profile: ResearchGate
  Profile: Zotero
  

• Past association:
  Honorary Assoc. Prof., School of IT, Deakin (August 2021-July 2024)
  Assoc. Prof. Dept of Info Sys and Bus Analytics, Deakin (2002-July 2021)
  Senior Lecturer, Dept of IS, University of Melbourne (1996-2001)
  Earlier appointments at La Trobe University, Telecom Research Labs, RMIT

• More here:
  Teaching, Projects, Research, Publications
  Awards, Presentations, Supervision, Media

Poster. Quantum time-series encoding and decoding (Click image)

Find more about an impact of encoding of time-series on the performance of quantum forecasting and curve fitting models by reading our poster paper, authored by Jacob L. Cybulski and Sebastian Zając, "The Art of Data Encoding and Decoding for Quantum Time Series Analysis," 8th International Conference on Quantum Techniques in Machine Learning University of Melbourne, Melbourne, Australia, 25-29 November 2024.

If you happen to be at QTML'2024, talk to us at the poster session 1 (stand 3), scheduled on 16:00-17:30, 26 Nov 2024. On the way back to other QTML presentations and events, you can also take with you a PDF copy of our poster.

The code and results, which guided the creation of the poster can be found in our GitHub repository. Note that as compared with the abstract available from QTML'2024 conference, the code and the poster represent an updated version 2 of the results - this is reflected as repo branch "ts_ed_v2".