Is there a quantum neural network "hello world" for character recognition (convolutional neural networks)?

Question

I'm making my way through several neural network examples and libraries. Landing on this nugget, in creating a basic NN for character recognition.

In calculating weights and biases, looking at the graph below, looks oddly familiar in one of the quantum-related problems to solve - minimizing cost functions and finding local minimums.

Is there an equivalent quantum NN for this and are there examples I can begin working with?

My interest here is in how a parallel quantum system can be more efficient with training data over its non-quantum counterpart.

Answer 1

I don't think a hello world really exists here. You can have different points of view or goals here. I will give references.

The first one is speeding up parts of the algorithm with a quantum version (here is an example reference). But here, we assume a perfect hardware.

Another one is to apply it to quantum many-body systems. The interesting point here is to have less parameters/weights to work on your problem.

The last one is to apply with quantum circuits that could be run on near-term devices. One way to do so would be to process the data amenable to a realistic-size on a quantum computer, using transfer leaning for instance, and apply a parameterized quantum circuit where its input correspond to the output of a classical NN. For this one, there exists an implementation here.