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.
