Published a paper (now accepted into JHEP!) on how quantum computers can be used to build neural network-esque models. It was a fun paper where we:
- built a variational quantum classifier - these are neural network-esque models that are designed to run on quantum computers
- combined this with a form of quantum gradient descent (which, unsurprisingly, is a quantum-y version of gradient descent)
- we found using this method allows us to build a network that can be smaller than a classical network (i.e. the quantum neural network needs less trainable parameters than a classical one, to get the same result!)
