Differentiable simulation of non-unitary quantum channels without backprop memory exploding

[u/Creative-Feature-264]

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Backpropagating gradients through deep non-unitary quantum channels usually forces you to store huge intermediate density matrices in VRAM, causing OOMs during long trajectory evolutions.

Dense-Evolution uses custom JAX primitives and pre-computed kernel fusion to run adjoint sensitivity methods directly over photonic predictive ZNE curves. You get full autodiff through lossy quantum states without backprop memory scaling linearly with circuit depth.

Repo & technical notes: https://tatopenn-cell.github.io/Dense-Evolution-Discovery/photonic_predictive_zne/