Reducing single-qubit gate complexity using machine-learned microwave pulses
Year: 2025
Type: article
Abstract: A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning.
Source: Academia quantum.
Authors: Jaden Nola, Uriah Sanchez, Anusha Krishna Murthy, Elizabeth Behrman, James Steck
Institution: Wichita State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410239198