Quantum Semi-Supervised Kernel Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningQuantum Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
Anomaly detection (AD) involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden pattern…
Anomaly DetectionQuantum Machine LearningregressionNon-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space. Developing quantum kernel function (QKF), which defines the distances among the samples …
Active LearningBIG-bench Machine LearningTensor NetworksEfficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when d…
Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionQuantum Semi-Supervised Learning with Quantum Supremacy
Quantum machine learning promises to efficiently solve important problems. There are two persistent challenges in classical machine learning: the lack of labeled data, and the limit of computational power. We propose a n…
BIG-bench Machine LearningClusteringQuantum Machine LearningSupervised quantum machine learning models are kernel methods
With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model wi…
BIG-bench Machine LearningQuantum Machine Learning