We can try compressing the baths with torch.eigh() or ~SVD.
Every bath element is a tensor with 3 legs $(\chi, mH_i, \chi)$ meaning 3d tensor, i.e. a list of Hermitian matrices $B(\chi, \chi)$ of length $mH_i$
These matrices can be decomposed with torch.eigh() $B_{\chi, \chi} = U S U^\dagger$, so that small eigenvalues(~singular values) can be chopped off, let's say 1e-10.
Again, this is a memory-performance trade. Maybe Multiplication $H_{eff} \psi$ will happen faster
We can try compressing the baths with$(\chi, mH_i, \chi)$ meaning 3d tensor, i.e. a list of Hermitian matrices $B(\chi, \chi)$ of length $mH_i$
torch.eigh()or ~SVD.Every bath element is a tensor with 3 legs
These matrices can be decomposed with$B_{\chi, \chi} = U S U^\dagger$ , so that small eigenvalues(~singular values) can be chopped off, let's say
torch.eigh()1e-10.Again, this is a memory-performance trade. Maybe Multiplication$H_{eff} \psi$ will happen faster