NISQ

In data-scarce domains, the bottleneck isn’t computing power — it’s labeled data. Semi-supervised learning (SSL) thrives here, using a small set of labeled points to guide a vast sea of unlabeled ones. But what happens when we bring quantum mechanics into the loop? This is exactly where Improved Laplacian Quantum Semi-Supervised Learning (ILQSSL) and Improved Poisson Quantum Semi-Supervised Learning (IPQSSL) enter the stage. From Graphs to Quantum States Both ILQSSL and IPQSSL operate in the graph-based SSL paradigm, where data points become nodes and similarity measures define edges. The twist is how they embed this graph structure directly into quantum states using QR decomposition. By decomposing graph-derived matrices into orthogonal (unitary-compatible) components, they map structure into quantum circuits without violating the unitarity constraints of quantum computing. ...

If quantum computing is the future, then quantum federated learning (QFL) is its decentralized heartbeat — promising data privacy, distributed intelligence, and unparalleled computing power. But like a high-performance car with faulty brakes, QFL’s potential is hindered by one chronic issue: quantum noise. A new paper introduces a deceptively simple yet powerful idea to address it — sporadic learning. In doing so, it doesn’t just offer a technical tweak — it reframes how we think about contribution and silence in distributed AI. ...