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Title: Quantum Semi-Supervised Kernel Learning

Paper content:
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.
Data sets used for training machine learning models are becoming increasingly large, leading to continued interest in fast methods for solving large-scale classification problems.
One of the approaches being explored is training the predictive model using a quantum algorithm that has access to the training set stored in quantum-accessible memory.
In parallel to research on efficient architectures for quantum memory (Blencowe, 2010) , work on quantum machine learning algorithms and on quantum learning theory is under way (see for example Refs.
(Biamonte et al., 2017; Dunjko & Briegel, 2018; Schuld & Petruccione, 2018) and (Arunachalam & de Wolf, 2017) for review).
An early example of this approach is Quantum LS-SVM (Rebentrost et al., 2014a) , which achieves exponential speedup compared to classical LS-SVM algorithm.
Quantum LS-SVM uses quadratic least-squares loss and squared-L 2 regularizer, and the optimization problem can be solved using the seminal HHL (Harrow et al., 2009 ) algorithm for solving quantum linear systems of equations.
While progress has been made in quantum algorithms for supervised learning, it has been recently advocated that the focus should shift to unsupervised and semi-supervised setting (Perdomo-Ortiz et al., 2018) .
In many domains, the most laborious part of assembling a training set is the collection of sample labels.
Thus, in many scenarios, in addition to the labeled training set of size m we have access to many more feature vectors with missing labels.
One way of utilizing these additional data points to improve the classification model is through semi-supervised learning.
In semi-supervised learning, we are given m observations x 1 , ..., x m drawn from the marginal distribution p(x), where the l (l m) first data points come with labels y 1 , ..., y l drawn from conditional distribution p(y|x).
Semi-supervised learning algorithms exploit the underlying distribution of the data to improve classification accuracy on unseen samples.
In the approach considered here, the training samples are connected by a graph that captures their similarity.
Here, we introduce a quantum algorithm for semi-supervised training of a kernel support vector machine classification model.
We start with the existing Quantum LS-SVM (Rebentrost et al., 2014a) , and use techniques from sample-based Hamiltonian simulation (Kimmel et al., 2017) to add a semisupervised term based on Laplacian SVM (Melacci & Belkin, 2011) .
As is standard in quantum machine learning (Li et al., 2019) , the algorithm accesses training points and the adjacency matrix of the graph connecting samples via a quantum oracle.
We show that, with respect to the oracle, the proposed algorithm achieves the same quantum speedup as LS-SVM, that is, adding the semisupervised term does not lead to increased computational complexity.

assistant: text: We extend quantum SVMs to semi-supervised setting, to deal with the likely problem of many missing class labels in huge datasets.