In Episode 146, Bob Coecke, Founder and CEO of Relational Intelligence returns alongside Alessandro Cheli, Categorical Machine Learning Operations at Relational Intelligence, to introduce two brand new companies. The bulk of the conversation covers Relational Intelligence, built on the idea that intelligence — human, mechanical, or otherwise — is better understood through relationships than through the reductive, static frameworks that dominate western scientific thinking. The team dig into relational modeling, the DisCoPy Python library for working with string diagrams and category theory, and why this approach produces AI that is interpretable and formally verifiable in a way neural networks are not. The conversation also touches on how the architecture is quantum-native from the ground up, meaning it won't need to be retrofitted when scalable quantum hardware arrives. Bob closes with a brief look at Quantum Entertainment Berlin, including a quantum guitar performance at Wacken Open Air and a VR game designed to teach quantum mechanics by being genuinely fun to play.

In Episode 145, Fred Chong returns to discuss what's new at Infleqtion, now a public company, and the work that's keeping him busy across both academia and industry. The team dig into Quantum for Bio, a multimodal cancer biomarker discovery project developed with Welcome Leap and now continuing with IBM, where hybrid quantum-classical algorithms are uncovering relationships between genes, RNA, and tumor imaging that classical methods haven't been able to tackle. Fred shares Peter Shor's perspective that the shortage of quantum algorithms reflects a shortage of good machines, not a shortage of ideas, and why that's an optimistic take. The conversation also covers neutral atoms as a maturing modality, Infleqtion's dual-species machine and how it reduces the need for physical qubit movement, and the concept of interleaving error-corrected qubit tiles to reduce long-range communication costs.

In Episode 144, Todd Brun, Professor of Electrical Engineering, Computer Science, and Physics at the University of Southern California, returns to break down where quantum error correction stands today. The team cover the transition from theoretical thresholds to real experimental demonstrations, why decoding latency matters as much as code quality, and the counterintuitive insight at the heart of quantum error correction — that you can measure what went wrong without measuring the data itself. Todd also weighs in on the trade-offs between modalities when it comes to implementing error correction in hardware, and closes with a look at how far the field has come since the early nineties when quantum computing was just an idea.