This virtual talk will introduce the 01 Quantum AI Marketplace concept and demonstrate an example of an FHE-encrypted decision tree. The demo will show a client logging in with private data, encrypting that data using fully homomorphic encryption, sending it to a model owner, and receiving encrypted inference results.

Decision trees are a useful starting point for encrypted inference because they are deterministic and interpretable: for a given input, the model follows a clear path to a prediction. In an FHE setting, however, that path cannot be followed with normal plaintext branching. Instead, the tree logic is transformed into encrypted comparisons, path scoring, and leaf selection, allowing the model owner to evaluate the decision tree without accessing the client’s raw data.

The client then decrypts the result and obtains the final prediction.
The second part of the talk will explain why FHE development is fundamentally different from regular AI software development. Unlike traditional applications, FHE systems cannot rely on normal branching over private data. Comparisons are difficult and often approximate, algorithms must be redesigned as mathematical circuits, and performance depends heavily on packing, rotations, multiplicative depth, and parameter selection.

Attendees will leave with a practical understanding of how encrypted inference works, why it matters for sensitive-data AI use cases, and what engineering challenges must be solved to make FHE-based AI systems production-ready. This session is intended for AI leaders, applied ML engineers, privacy engineers, and technical decision-makers exploring secure AI deployment models.