The role of entanglement for enhancing the efficiency of quantum kernels towards classification
arXiv:2209.05142 · doi:10.1016/j.physa.2023.128938
Abstract
Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.
References in corpus (11)
- Thumbs up? Sentiment Classification using Machine Learning Techniques
- Quantum discord and the power of one qubit
- Quantum algorithms for quantum chemistry and quantum materials science
- Unsupervised Machine Learning on a Hybrid Quantum Computer
- A classification of entanglement in three-qubit systems
- Supervised quantum machine learning models are kernel methods
- Importance of Kernel Bandwidth in Quantum Machine Learning
- Quantum Shuttle: Traffic Navigation with Quantum Computing
- Quantum-inspired Complex Convolutional Neural Networks
- Unsupervised quantum machine learning for fraud detection
- Quantum Unsupervised and Supervised Learning on Superconducting Processors