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.