Project Description

It is well known that quantum computing has the potential to offer exponential computational speedup for some problems. In fact, google has recently demonstrated near "quantum supremacy" on a task using it's own quantum computer. While there has been a surge in interest in quantum computing, there still remain many practical and theoretical problems to be explored in order for us to be able to fully exploit the potential of quantum computers. There will be two projects available in the vein of this exploration.

Optimal Network Structure for Perfect State Transfer: PST is potentially an important concept for information storage and transfer in a quantum computer, and the focus of this project would be finding realistic network structures which would allow for multiple perfect state transfer. Particular focus would be paid to the recent idea of orientable networks or directed networks on which PST can occur. This project will require simulation of the network induced Hamiltonian solution to the Schrodinger equation to explore which systems produce PST or almost PST.

Quantum Machine Learning Algorithms: In machine learning side many algorithms require making approximations, or using monte-carlo sampling thanks to exponential scaling in computational cost, such costs could be greatly reduced in a quantum computer. Thus in this project the development of quantum machine learning algorithms, particularly in Qiskit, will be explored. The performance of quantum machine learning data will be compared to classical computation performance on simulated datasets where ground truth is known.