WEEK 1
PROJECT INTRODUCTION
Artificial neural networks are a class of computational models whose architecture is biologically inspired. They are useful in topics such as pattern recognition, image classification, and decision making. In the paper “An artificial neuron implemented on an actual quantum processor” by Tacchino et al., a quantum information-based algorithm was used to implement a “qubit neuron” (quantum binary neuron). This type of artificial neuron has each qubit (the computational unit in quantum computers) acting as an individual neuron within the network. A 2 qubits version of the algorithm was implemented on the IBM quantum processor and used in a pattern recognition process. The results show an exponential advantage in terms of storage capabilities over classical perceptron (a simple neuron) models and can be trained in a hybrid quantum-classical scheme.
The focus for my honours project is to investigate and attempt to replicate this experiment. I would like to explore other papers with similar content in relations to quantum computing and machine learning. I would also like to build a circuit similar to the one outlined in the paper by using IBM Q quantum processor and Qiskit. As I look into this topic, I hope to find out more about the quantum information-based algorithm and how best to implement it. I hope that I can contribute towards the research on practical quantum neural networks, with the hope of implementation on quantum processing hardware.