Quantum Machine Learning

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. ...

Residual Entanglement: How ResQuNNs Fix Gradient Flow in Quantum Neural Networks In classical deep learning, residual connections revolutionized the training of deep networks. Now, a similar breakthrough is happening in quantum machine learning. The paper “ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks” introduces a method to overcome a fundamental bottleneck in Quantum Convolutional Neural Networks (QuNNs): the inability to train multiple quantum layers due to broken gradient flow. ...