# Finding and Testing a Practical Use of NISQ Era Quantum Computing

## School Name

South Carolina Governor's School for Science and Mathematics

## Grade Level

12th Grade

## Presentation Topic

Computer Science

## Presentation Type

Mentored

## Abstract

A quantum bit's ability to be in a superposition of 0 and 1 solves many problems that were otherwise impractical or impossible with classical computers. However, current quantum hardware is limited at scale and cannot perform many of these breakthrough quantum algorithms. Smaller scale quantum algorithms are often used as an alternative but lack real use. One practical use of current state quantum hardware is machine learning which uses self-learning models to derive information from datasets and predict outcomes. As a continuation of practical quantum computing, my research group implemented a variational quantum classifier to predict heart attacks. Data was preprocessed classically to determine which parameters had the highest correlation to our target variable of whether the patient had a heart attack. These parameters were then normalized for the quantum space with a feature mapping process. The newly normalized data was fed through a variational circuit of random parameters. These parameters were then updated through an optimization process of 30 iterations for each data input. This fine tuning of the parameters yielded a quantum model that was able to predict heart failure. Our algorithm implementation was constrained to a quantum simulation on a classical computer. This eradicated the theoretical quantum speedup that would be present from running on real quantum hardware. Fortunately, our implementation can run on existing quantum machines and provides a practical use of quantum computing.

## Recommended Citation

Sackmann, Grant, "Finding and Testing a Practical Use of NISQ Era Quantum Computing" (2023). *South Carolina Junior Academy of Science*. 30.

https://scholarexchange.furman.edu/scjas/2023/all/30

## Location

ECL 340

## Start Date

3-25-2023 9:30 AM

## Presentation Format

Oral Only

## Group Project

No

Finding and Testing a Practical Use of NISQ Era Quantum Computing

ECL 340

A quantum bit's ability to be in a superposition of 0 and 1 solves many problems that were otherwise impractical or impossible with classical computers. However, current quantum hardware is limited at scale and cannot perform many of these breakthrough quantum algorithms. Smaller scale quantum algorithms are often used as an alternative but lack real use. One practical use of current state quantum hardware is machine learning which uses self-learning models to derive information from datasets and predict outcomes. As a continuation of practical quantum computing, my research group implemented a variational quantum classifier to predict heart attacks. Data was preprocessed classically to determine which parameters had the highest correlation to our target variable of whether the patient had a heart attack. These parameters were then normalized for the quantum space with a feature mapping process. The newly normalized data was fed through a variational circuit of random parameters. These parameters were then updated through an optimization process of 30 iterations for each data input. This fine tuning of the parameters yielded a quantum model that was able to predict heart failure. Our algorithm implementation was constrained to a quantum simulation on a classical computer. This eradicated the theoretical quantum speedup that would be present from running on real quantum hardware. Fortunately, our implementation can run on existing quantum machines and provides a practical use of quantum computing.