Quick Navigation
Topics
Quantum Optimization
Quantum Machine Learning
Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting
arXiv
Authors: Maksims Dimitrijevs, Mārtiņš Kālis, Iļja Repko
Year
2024
Paper ID
6263
Status
Preprint
Abstract Read
~2 min
Abstract Words
80
Citations
N/A
Abstract
Time-series forecasting is essential for strategic planning and resource allocation. In this work, we explore two quantum-based approaches for time-series forecasting. The first approach utilizes a Parameterized Quantum Circuit (PQC) model. The second approach employs Variational Quantum Linear Regression (VQLS), enabling time-series forecasting by encoding the problem as a system of linear equations, which is then solved using quantum optimization techniques. We compare the results of these two methods to evaluate their effectiveness and potential advantages for practical forecasting applications.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- Time-series forecasting is essential for strategic planning and resource allocation.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.