A Quantum Neural Network for Noisy Intermediate Scale Quantum Devices

Abstract: Neural networks have helped the field of machine learning grow tremendously in the past decade, and can be used to solve a variety of real world problems such as classification problems. On another front, the field of quantum computing has advanced, with quantum devices publicly available via the cloud. The availability of such systems has led to the creation of a new field of study, Quantum Machine Learning, which attempts to create quantum analogues of classical machine learning techniques. One such method is the Quantum Neural Network (QNN) inspired by classical neural networks. In this thesis we design a QNN compatible with Noisy Intermediate Scale Quantum (NISQ) devices, which are characterised by a limited number of qubits and small decoherence times. Furthermore we provide an implementation of the QNN classifier using the open source quantum computing software development kit, Qiskit provided by IBM. We perform a binary classification experiment on a subset of the MNIST data set, and our results showed a classification accuracy of 80.6% for a QNN with circuit depth 20.