Quantum machine learning methods have the potential to facilitate learning using extremely large datasets. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels. One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors. Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines. The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.
This paper proposes to extend a quantum-computing based solution of least-squares support-vector-machine to include use of unlabeled samples.  The formulation is analogous to the classical-computing case, in which semi-supervised learning introduces an additional term in the system of equations, which the authors show how to compute in the quantum setting without degrading big-O complexity.
I would lean toward rejecting this paper,  primarily on account of how the contribution relates to the publication venue.  The primary contribution lies in the procedure for preparing and propagating the quantum mechanical states needed to compute on the additional term.  Although the application is machine learning, the technique itself is still rather afar from this topic and would not appear to be of general benefit to conference-goers outside of quantum computing.  The overwhelming majority of quantum machine learning references in this paper appear in physics journals (all but one, which was ICML 2019).  Most of this paper is background material, which yet remains inadequate to convey insights into design decisions in the details of their main contribution, the derivations in section 3.2.  (I have a background in physics but not quantum computing.)   
Perhaps a paper organization more amenable to this venue would be to shift some of the lengthier equations into an appendix and use the space of the paper to discuss a more conceptual and contextual understanding of why this technique is desirable, at each step, relative to other possible quantum techniques.  For example, Figure 1 is not explained, and is not decipherable to someone outside the field, so doesn't itself add to the story.
Could be really good work, but the presentation doesn't quite come across.
EDIT:  See comments to do with paper revision, which significantly improved the presentation.