Speakers
Description
Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data (verifiability) and ensuring the privacy of the learner’s conclusions (covertness). Inspired by the classical covert (verifiable) learning model of Canetti and Karchmer (TCC 2021), we propose the first models of covert verifiable learning in quantum learning theory and realize them without computational hardness assumptions for remote data access scenarios motivated by established quantum data advantages. Namely, we design covert algorithms for:
(1) learning quadratic functions from public quantum examples and private quantum statistical queries;
(2) Pauli shadow tomography and stabilizer state learning from public multi-copy and private single-copy quantum measurements;
(3) verifiably solving Forrelation and Simon's problem from public quantum queries and private classical queries when the adversary is an i.i.d. ancilla-free eavesdropper.
Our models and corresponding algorithms demonstrate that important, large quantum advantages in learning are privately and verifiably achievable even with untrusted, remote data.
| Topic | Theoretical foundations of quantum learning |
|---|---|
| Additional Topic | Quantum state reconstruction from data |