UC Santa Cruz

Quantum Machine Learning is a rapidly growing field in the applied and theoretical machine learning community. A number of novel techniques are proposed for integrating quantum machine learning into classical machine learning methods and paradigms, including quantum self-attention in Vision Transformers, using parameterized unimodular matrices for quantum-classical hybrid machine learning models, and various ways of inserting quantum machine learning into spiking neural networks. The above techniques are trained and tested on standardized datasets used within the machine learning community. All techniques presented are evaluated​ against comparable classical models​.