Amboss Research enables Machine Learning to optimize payments on the Bitcoin Lightning Network
NASHVILLE, Tenn., May 22, 2024 /PRNewswire-PRWeb/ -- Amboss Technologies, a payment operations leader for Bitcoin's Lightning Network, released novel research today using graph machine learning to enhance the efficiency and scalability of bitcoin payments on the Lightning Network. The study, titled "Channel Balance Interpolation in the Lightning Network via Machine Learning," reveals new methods to improve payment success using a combination of graph theory and machine learning.
The Bitcoin Lightning Network is a Layer 2 protocol used by Coinbase, Binance, and Block (formerly Square) that brings rapid, low-cost transactions to bitcoin. As more exchanges have adopted the Lightning Network, payment reliability has been a challenge for the LN. Traditionally, wallets and exchanges use trial and error "probes" to find a payment route through Lightning's peer to peer payment network. Amboss Technologies' latest research, however, introduces a machine learning approach that could transform how payments are routed through the network.
"As bitcoin takes center stage in finance, it's essential that we continue to innovate on bitcoin scaling technology that further enhances its user experience. Lightning is a critical element to scaling bitcoin and payment reliability has been its biggest problem. Our research enables scalable, machine learning methods to be an answer," said Jesse Shrader, CEO of Amboss Technologies.
Collaborating with Emanuele Rossi from VantAI and Vikash Singh from Stillmark, the Amboss team evaluated several machine learning models against traditional heuristic methods. The results were clear: machine learning not only outperforms existing approaches but also offers new insights into better payment pathfinding.
"Our research indicates that machine learning models can predict channel balances with significantly higher accuracy than existing methods," said Vincent, Senior Data Scientist at Amboss Technologies. "By integrating these models into the network's pathfinding algorithms, we can streamline operations and reduce payment failure rates, making Lightning an even more robust option for everyday payments."
This breakthrough has substantial implications for users of the Bitcoin Lightning Network. Improved pathfinding efficiency could lead to wider adoption of the network, enhancing the overall utility of Bitcoin as a payment system, and setting the stage for future ML automation.
Amboss Technologies is committed to further refining these models and integrating them into real-world applications. The company plans to incorporate the research findings into its suite of payment operations tools, which are used by some of the largest operators in the Lightning Network.
Amboss Technologies is at the forefront of applying machine learning to one of the most critical issues facing cryptocurrencies today: scalability. As the Lightning Network grows, Amboss is well-positioned to become essential for institutional adoption of the LN.
The full research paper has been published and is available for review. Amboss Technologies invites interested parties to explore the detailed findings and discuss integration and collaboration opportunities.
To learn more about Amboss' research on machine learning-powered pathfinding, visit https://rpo.dev/pathfinding.
For media inquiries, please contact Phil LeRoy at (310) 260-7901 or phil(at)melrosepr(dot)com.
About Amboss Technologies
Amboss is a leading provider of payments operations for the Bitcoin Lightning Network, using machine learning to offer optimized routing and tools that redefine decentralized finance. Amboss supports businesses and consumers in improving connections and payment routes based on cost, reliability, or reputation as well as enterprise-grade risk management. Amboss operates Magma, the leading marketplace for lightning liquidity and non-custodial yield.
Media Contact
Phil LeRoy, Melrose PR, (310) 260-7901, [email protected], https://www.melrosepr.com/
SOURCE Amboss Technologies
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