6 papers
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
Harrison Rush, Vincent Davis, Simone Antonelli +3
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a bu…
Resource-efficient energy-based operator selection in fermionic ADAPT-VQE via exact Hamiltonian transformation
Emanuele Rossi, Erik Rosendahl Kjellgren, Artur F. Izmaylov +3
The energy-based approach to operator selection in ADAPT-VQE relies on reconstructing the one-parameter energy landscape for each operator in the pool. In fermionic implementations…
Predicting Channel Closures in the Lightning Network with Machine Learning
Simone Antonelli, Vincent Davis, Harrison Rush +4
The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN can be closed either by mutu…
Communicating Sound Through Natural Language
Emanuele Rossi, Emanuele RodolÃ
Natural language is widely used to describe, prompt, and control audio systems, but rarely serves as the representation carrying audio itself. We introduce lexical acoustic coding…
Channel Balance Interpolation in the Lightning Network via Machine Learning
Vincent Davis, Emanuele Rossi, Vikash Singh
The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin's scalability by facilitating quick and cost effective transactions through payment channels. Thi…
Bayesian Binary Search
Vikash Singh, Matthew Khanzadeh, Vincent Davis +4
We present Bayesian Binary Search (BBS), a novel probabilistic variant of the classical binary search/bisection algorithm. BBS leverages machine learning/statistical techniques to…