Semidefinite optimization of the quantum relative entropy of channels

This project represents a high-level theoretical contribution to quantum information science. While the star count (0) reflects its niche academic nature rather than a lack of utility, the defensibility is high due to the 'deep domain expertise' required to implement and validate these specific discretization schemes for channel entropy. Frontier labs (OpenAI, Anthropic) are unlikely to compete here as this serves the Quantum Computing R&D niche, specifically in resource theories and channel discrimination. The primary 'competitors' are general-purpose conic solvers like Hypatia or specialized toolkits like CVXQUAD and QETLAB. This project carves a moat by solving a specific optimization bottleneck—handling the maximization of relative entropy for channels—which is mathematically more complex than the state-based equivalent. The risk is primarily academic displacement if a more efficient approximation technique is discovered, but for current quantum researchers, this provides a necessary infrastructure-grade algorithm.