  This paper explores the potential benefits of quantum coherence and quantum
discord in the non-universal quantum computing model called deterministic
quantum computing with one qubit (DQC1) in supervised machine learning. We show
that the DQC1 model can be leveraged to develop an efficient method for
estimating complex kernel functions. We demonstrate a simple relationship
between coherence consumption and the kernel function, a crucial element in
machine learning. The paper presents an implementation of a binary
classification problem on IBM hardware using the DQC1 model and analyzes the
impact of quantum coherence and hardware noise. The advantage of our proposal
lies in its utilization of quantum discord, which is more resilient to noise
than entanglement.
