As the title states.
I am a Machine Learning Engineer with a background in physics & engineering (post-secondary degrees). I am reading the Tensorflow Quantum paper. They say the following within the paper:
One key observation that has led to the application of quantum computers to machine learning is their ability to perform fast linear algebra on a state space that grows exponentially with the number of qubits. These quantum accelerated linear-algebra based techniques for machine learning can be considered the first generation of quantum machine learning (QML) algorithms tackling a wide range of applications in both supervised and unsupervised learning, including principal component analysis, support vector machines, kmeans clustering, and recommendation systems. These algorithms often admit exponentially faster solutions compared to their classical counterparts on certain types of quantum data. This has led to a significant surge of interest in the subject. However, to apply these algorithms to classical data, the data must first be embedded into quantum states, a process whose scalability is under debate.
What is meant by this sentence However, to apply these algorithms to classical data, the data must first be embedded into quantum states?
Are there resources that explain this procedure? Any documentation or links to additional readings would be greatly appreciated as well.
Thanks in advance!
Note: I did look at this previous question for reference. It helped. But if anyone can provide more clarity from a more foundational first principles view (ELI5 almost), I would be appreciative