I have been investigating uses for quantum machine learning, and have made a few working examples (variations of variational quantum classifiers using PennyLane). However, my issue now is its relationship with classical machine learning. At the moment (in my tests, at least), QML seems to not provide any major improvement in performance (compared to a classical network) and is significantly slower when running on real hardware.
I understand that this is a young field people are still exploring, but I'm curious as to why you would not just always use a classical ML algorithm for problems. Therefore, my questions are:
- What benefits (or predicted benefits) are there using quantum machine learning?
- Is there little benefit now, but the potential for performance increases when hardware improves?
- It wouldn't surprise me to learn there are examples where QML outperforms classical ML. Here, my question is why is this the case? How would moving to a quantum regime improve performance?