Multi-class Classification Model Inspired by Quantum Detection Theory
This work addresses classification tasks in machine learning by proposing a quantum-inspired approach, but it appears incremental as it builds directly on prior binary classification work.
The authors tackled the problem of multi-class classification by extending a binary classifier based on quantum detection theory to handle three or more classes, resulting in a new model that aims to improve effectiveness over classical methods.
Machine Learning has become very famous currently which assist in identifying the patterns from the raw data. Technological advancement has led to substantial improvement in Machine Learning which, thus helping to improve prediction. Current Machine Learning models are based on Classical Theory, which can be replaced by Quantum Theory to improve the effectiveness of the model. In the previous work, we developed binary classifier inspired by Quantum Detection Theory. In this extended abstract, our main goal is to develop multi-class classifier. We generally use the terminology multinomial classification or multi-class classification when we have a classification problem for classifying observations or instances into one of three or more classes.