NISQ

TL;DR for operators Labels are expensive. That is the clean business problem behind this paper. In healthcare, credit review, fraud triage, and scientific classification, organisations often have many observations and too few trusted labels. Semi-supervised learning tries to stretch those scarce labels across the structure of the data rather than pretending every missing label is merely a procurement problem with a nicer dashboard. ...

TL;DR for operators SpoQFL is a proposal for making quantum federated learning less fragile by teaching noisy clients when to speak and when to stay quiet.1 In ordinary federated learning, each client trains locally and sends model updates to a server. In quantum federated learning, those clients are quantum models running under noisy intermediate-scale quantum conditions, which means their updates can be corrupted by gate errors, measurement uncertainty, decoherence, and client-to-client hardware variation. ...