Quantum Machine Learning Explained & Basic Example of Classification Problem
(Medium) Quantum Machine Learning (QLM) is the combination of Quantum Computing and Machine Learning.
Machine Learning is nothing but to train the machine(computer) with the help of a lots of data and make the computer find some pattern from our data and apply the finding to the new set of data. You can think this like how a baby learns to talk. He/she hears a lot of word from our surroundings in different situation many times and learn when to speak what day by day. It’s a continuous process, it lasts for lifetime.
Quantum Machine Learning is nothing but when we do the ML computation on quantum computers or rather in quantum instances instead of classical computers.
The author Deep Dutta gives a basic example of classification problem where Quantum Machine Learning overperforms Classical Machine Learning. I am going to use Support Vector Machine(SVM) algorithm here. The basic difference between classical SVM and QSVM(Quantum SVM) is classical SVM runs on classical instances and Quantum SVM runs on quantum instances.
Dutta provides an implementation where Quantum Machine Learning clearly overperforms Classical Machine Learning. For simplicity we are going use a basic dataset namely ad-hoc dataset.