AI Article Synopsis
- The study addresses the challenge of training quantum neural networks (QNNs) by introducing the Bees Optimization Algorithm (BOA) to tackle barren plateaus.
- Experiments show that BOA outperforms the Adam algorithm in terms of convergence speed, accuracy, and computational efficiency across different qubit counts and circuit depths.
- The findings suggest that BOA could significantly improve the use of QNNs in complex quantum computations.
Article 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 to enhance the applicability of QNNs in complex quantum computations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1063/5.0240466 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!