For humans, categorizing things we see is a daily task. We, humans, subconsciously categorize everything we see in our everyday life. Within milliseconds we can separate tables from chairs, apples from oranges, and your friends from strangers. For a computer, this is not so trivial. Telling the difference between a cat and a dog by looking at a picture is an almost impossible task for a computer algorithm.
Machine learning seeks to fill this gap in our computational abilities, by gathering as many examples of the desired behavior as possible and searching an immense computational space for a mathematical function which will mimic that behavior. However, this currently requires a large amount of computing power. Will this be more suitable for a quantum computer?
Can you think of data you'd want to apply Quantum Machine Algorithms to?
It might be interesting to take a peek at the original paper of this HHL algorithm, as well as two applications of it:
Additionally, these are two websites that further discuss the possibility of applying this Quantum Machine learning algorithm.
- Can classical big data be loaded fast enough to achieve linear algebra based quantum machine learning speed-ups?
- What could be the possible future applications for the HHL algorithm?