I'm new to quantum machine learning, and I wanted to know how quantum data is processed in a quantum neural net.

For example, if I am training a QNN to classify entangled circuits from non-entangled circuits, then the data is circuits itself, so how would the entangled and non-entangled circuits get extracted as input for the ansatz to rely on and tune?

Additionally what output value would be extracted at the end for the cost function and for the optimizer to adjust?

For example, if you were to extract the expectation value of a qubit, wouldn't that already determine the prediction? How would the loss be calculated and the optimizer adjusted?



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