Quantum Semi-Supervised Kernel Learning

Abstract

Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset. Thus, 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. Through a theoretical study of the algorithm's computational complexity, we show that it maintains the same speedup as the fully-supervised Quantum LS-SVM.

References in corpus (17)

• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Quantum support vector machine for big data classification
• Quantum principal component analysis
• Efficient quantum algorithms for simulating sparse Hamiltonians
• Quantum Data Fitting
• Laplacian Support Vector Machines Trained in the Primal
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• A quantum-inspired classical algorithm for recommendation systems
• Quantum-inspired algorithms in practice
• Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
• Hamiltonian Simulation with Optimal Sample Complexity
• Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension
• A Survey of Quantum Learning Theory
• Sublinear quantum algorithms for training linear and kernel-based classifiers
• Quantum Sparse Support Vector Machines
• A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time

Cited by in corpus (2)

• Kernel-based quantum regressor models learn non-Markovianity
• Benchmarking of quantum fidelity kernels for Gaussian process regression