Novel quantum approaches to hyperdimensional computing for neural networks

In this work, we introduce new quantum machine learning models that combine both quantum and hyperdimensional computing. We focus our effort on two novel architectures that are first theoretically demonstrated, and then applied for testing to prototypical machine learning tasks, namely for pattern completion, classification, and clustering. We obtained accurate and promising results that prove, for the first time, the synergies between two of the most innovative computational approaches such as quantum computing and hyperdimensional computing.