Reducing single-qubit gate complexity using machine-learned microwave pulses Jaden Nola [1] , Uriah Sanchez [2] , Anusha Krishna Murthy* [3] , Elizabeth Behrman [1] , James Steck [4]

https://www.academia.edu/3064-979X/2/2/10.20935/AcadQuant7692 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.