Hello I’m currently working on a project where I’m trying to classify arrays with shape (8,60).
My basic plan was to use 8 qubits and 60 layers to embed the data (approximately 1Gb). But I run into an issue while doing the backpropagation with pytorch my RAM seems to fill up pretty quickly and that leads to a crash of my processes.
My computer is a OMEN (16Gb of RAM, GTX2060, 6Gb VRAM, intel i7 9th gen) It seems like it’s a performance issue of either my computer or the QPU wich is the basic pennylane simulator for now. It seems to be working if I reduce the numbers of layers to 1 but that ruins my model.
From what I understand it seems that when doing the backpropagation the QPU uses the RAM for calculation instead of the GPU wich leads to a crash. Is there a way to run a simulator on the GPU maybe ?
I’m really lost and my deadline is coming pretty fast. Is there a way to speed up the calculation maybe? Or the technology is not quite there yet ?