… quantum–classical architecture for self-supervised learning and demonstrate a numerical
advantage in the learning … We train quantum and classical neural networks together, such that …

… of quantum NLP models on the representation level. Specifically, we present a self-supervised
pre-training approach to train quantum encodings of sentences, and fine-tune quantum …

… , a graph self-supervised QAS that trains a predictor by self-supervised learning. Specifically,
… number of unlabeled quantum circuits, aiming to generate meaningful representations of …

… quantum bits often describe quantum neural network models. In this article, a novel self-supervised
shallow learning … -inspired quantum information system, referred to as quantum fully …

… This work pioneers the integration of self-supervised learning based on intrinsic quantum
circuit properties into QAS predictor development, providing a promising direction for efficient …

… quantum fidelity, quantifying similarity between quantum … QSEA outperforms existing quantum
self-supervised methods on … quantum learning systems and advances the development of …

… Quantifying information using quantum algorithms leads to enhanced automation processes
… Self-supervised learning is a paradigm in which models can learn from unlabelled data by …

… In this context, quantum machine learning has emerged as a … This work introduces a novel
quantum self-supervised relational … data through temporal relational learning. By integrating …

… D selfsupervised tensor neural network in quantum formalism … 3-D quantum-inspired
self-supervised tensor neural network (… contains quantum neurons designated by qubits or …

… quantum physics to represent hierarchical polysemy in a taxonomy. Our key contributions
are as follows: We first construct training data using a self-supervised … a quantum modeling …