Quick Navigation
Topics
Quantum Optimization
Quantum Machine Learning
Bee-yond the Plateau: Training QNNs with Swarm Algorithms
arXiv
Authors: Rubén Darío Guerrero
Year
2024
Paper ID
64185
Status
Preprint
Abstract Read
~2 min
Abstract Words
87
Citations
N/A
Abstract
In the quest to harness the power of quantum computing, training quantum neural networks (QNNs) presents a formidable challenge. This study introduces an innovative approach, integrating the Bees Optimization Algorithm (BOA) to overcome one of the most significant hurdles - barren plateaus. Our experiments across varying qubit counts and circuit depths demonstrate the BOA's superior performance compared to the Adam algorithm. Notably, BOA achieves faster convergence, higher accuracy, and greater computational efficiency. This study confirms BOA's potential in enhancing the applicability of QNNs in complex quantum computations.
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.
- In the quest to harness the power of quantum computing, training quantum neural networks (QNNs) presents a formidable challenge.
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.