Projects and research
Quantum Finance Module Library
One of our first projects will be to create a library of functions that combine parts of quantum computing and qauntitative finance to make working with quantum finance algorithims more accsesible. As of now the committee is in its infancy, but as we progress on this project updates will be posted here.
Research Into Applications
Part of what our committee will research is ways to apply quantum technology to financial technology. This entails looking into what areas of finance would benifit from quantum technology, and what areas should remain classical. Examples of this would be using quantum and financial modules to create hypothetical financial algorithims, and using our on campus quantum computer to test them. This area of research is for those more interested in the financial aspect of the committee.
Research Into Limitations
Modern quantum computers face many issues that make their current use in finance not viable. The aim of this part of our committee is to research ways to improve quantum computing software and hardware to solve these issues and make quantum technology viable for use in finance. This area of research is for those more interested in the quantum technology aspect of the committee
topics of research
This section will update regularly as the committee grows
Financial data sets are often times very large and constantly changing. For a quantum computer to process data, this data needs to be transformed so each data set can be represented by a superpostion of states. Current software and hardware limitations prevent us from managing this data sets using quantum technology. On the software front, current methods of data transformation introduce complexity to algorithims. Research into new ways to transform financial data that minimize complexity is essential for using quantum technology in finance. On the hardware front, modern quantum computers are very prone to error, and are unable to run for very long. As potential for error increased with the size of data sets, the error prone nature of quantum technology hinders its use in finance. The committee researches ways to minimize error while working with financial data, to allow quantum computers to work with more data for a longer time.
A significant use of technology in finance is for algorithm trading. As the name implies, this is where algorithms trade options instead of humans. A significant benefit of algorithm trading is the superior speed and precision when compared to human traders. The superior speed of quantum algorithms, compared to classical algorithms, makes quantum technology very appealing in algorithm trading. This area of research aims to create hypothetical ‘hybrid’ algorithms with both quantum and classical components. While quantum computing may be faster in some cases, there are also situations where a classical algorithm may be faster or more resource-effective. Developing hybrid algorithms aims to create an algorithm that optimizes speed and use of quantum computing by combining classical and quantum technology (because quantum computers can be costly!)