Quantum-Inspired AI and Tensor Network Compression with Román Orús

Tensor networks were invented to study quantum entanglement in condensed matter physics — so how did they end up compressing billion-parameter language models and running inside a live LLM on an IBM quantum processor? Román Orús, co-founder and Chief Scientific Officer of Multiverse Computing, traces the full intellectual journey from matrix product states to production AI, and gives an unusually candid account of where quantum-inspired computation ends and genuine quantum computation begins. If you're trying to understand what "quantum AI" actually means beneath the marketing, this is the episode to start with.