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It sounds like you are on the right track If you have more variables, yes you’ll need more wires. It doesn’t necessarily have to be 4 - there are different ways to encode, or embed input data into a quantum circuit. PennyLane includes a couple methods already built in in our templates. In the specific tutorial you linked, the
Displacement gate is being used for this purpose. So a good place to start would indeed be to use 4 wires, and then for each input variable, assign it to a qubit, and displace that qubit by the input amount.
In terms of what gates to apply, you’ll need to choose a model, or ansatz circuit for the network. The circuit in the tutorial is a single-mode version of the more general CVNeuralNetLayers, so I’d recommend giving that a go.
As a final point, the model in the tutorial is a continuous-variable model; you could also check out qubit-based models. The variational classifier tutorial gives a similar example with a qubit system that may be useful to explore.
Hope that helps, please let us know if you have any further questions!