Quantum support vector machines for classification and regression on a trapped-ion quantum computer

Understand

Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning.

• In this work, we examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR).
• We investigate these models using a quantum-circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor.
• For the QSVC tasks, we use a dataset containing fraudulent credit card transactions and image datasets (the MNIST and the Fashion-MNIST datasets); for the QSVR tasks, we use a financial dataset and a materials dataset.