TeNPy Forum

Yes, I think you need to recreate the environments from scratch.
Not completely sure, but I think you could try using the old ones as an initial guess for the new environments.

The disentanglers implemented in TeNPy act on two or more consecutive sites in the MPS language, but of course you can try to apply a bunch of them one after the other. It also has code that tries to bring a few sites next to each other and then disentangle them, but in my experience that didn't wor...

You just need to import the file in python, which contains the model definition; then you can just use the "name" of the model in the simulation parameters. To import that file with the definition, you have two options: Either you use the command line option -1 of tenpy-run or python -m te...

To be clear, the actual convergence of DMRG is much faster than just 1/(#sweeps performed), as you see on the right - there's a log-scale on the yaxis

is not the energy you want to study for convergence' it is the total energy of the system that contains age sites, including all energy terms in the environments with whatever state you had at that point. Pictorially, the "total" system that infinite DMRG grew after a couple of sweeps look...

A bit late, but better late than never ;-) apply_local_op accepts an operator that acts on n neighboring sites, for example for two sites with legs 'p0', 'p1', 'p0*', 'p1*' . You can use the brand-new function tenpy.networks.site.kron to create the corresponding bond operator from the single-site op...

You shouldn't trust expectation values/correlation functions that were calculated from an MPS that is not in canonical form, because these functions implicitly assume that psi is in canonical form. When your DMRG is really converged and doesn't change the state during the sweep any more, you would e...

The answer comes probably a bit too late, but anyways: How do you want to "fix" the states outside? If you imagine to project them to the local basis states with exactly 0/2 sites, you would have a product state with the rest. The hopping term wouldn't contribute, and you can just calculat...

They will only depend on the charge conservation you're using.

I guess you're aware of this, but just to be clear: when you do this, you write down all 2^L states, so you could just as well use exact diagonalization....

Hi Pablo, there is a way to do it more effiently, although I only did that numerically: I created the AKLT model by just defining bond terms, and then initializing the MPO from these bond terms - there's a function in TeNPy to automatically do the SVD of the bond term to get the MPO. Take a look at ...

It's a problem of the inital state and charge conservation, not the jordan wigner strings. TwoSiteDMRG updates two sites in the subspace created by projecting H to the current psi (i.e. your initial state) on other sites; this projection will render H zero because each term in H has at least two C/C...

Yes, that's right.
I tried to explain this in the section "spinful fermions" of the Jordan-Wigner user guide you referenced.
If you have a suggestion how to formulate this a bit clearer, feel free to submit a corresponding pull request on github!

rubenv Good catch, the fix is of course correct. Let me explain: The variation compression needs a copy of the old psi to compress against, and that copy is made during the class intialization, more precisely in [tenpyclass=tenpy.algorithms.mps_common.VariationalApplyMPO]init_env[/inline]. So it mo...

Whoa, I wasn't aware that you want to run it for non-hermitian H. Lancos implicitly assumes that H is hermitian! For that reason at least TeNPy's implementation of TDVP (which uses Lancos to do the local time evolution) does not work for that case! Dropping the hermiticity of H is in general very da...

Hi Ruben, you're right, the DMRG code supports segment boundary conditions already. Right now, you need to extract the left and right environment from a previous DMRG run, and then specify it in resume_data['init_env_data'] to the subsequent DMRG run by hand. There's still some subtleties, e.g. how ...

Update:
when trying to install TeNPy on a cluster with the Intel Python distribution, we encountered the same runtime warnings.
Looking into this now...

Sorry, I didn't realize you were looking at infinite MPS. For an infinite MPS, the leg between unit cells is non-trivial, and in general changes when you do a time evolution. This is where the error is coming from: when you initialize the MPSEnvironment, it needs to know how to connect the left-/rig...

For single-site DMRG to work you need to enable the "mixer" by setting dmrg_params['mixer']=True , otherwise it cannot grow the bond dimension. Be warned that the default mixer_params might still disable the mixer too early for larger/more correlated systems. I've just released a bugfix ve...

Yes: just run DMRG with an initial state in the symmetry sector you're interested in.
DMRG doesn't switch charge sectors, so the charge sector of the final state is determined by your initial state.

There are more ways to get the expectation value \(E = \langle \psi | H | \psi\rangle \) of a given state than just through H_bonds: when you have a model, you can evaluate the MPO expectation value with M.H_MPO.expectation_value(psi).
Does this answer your question?

Would you mind to provide a traceback? An "incompatible LegCharge" means it's trying to contract two legs with different charges somewhere, not necessarily in the MPS bonds.

To be clear: you need to evolve this for much longer than just a few steps, and continuously take measurements for C_ij(t) . In the end, you can then fourier-transform C_ij(t) , both in space and time, to get C_k(\omega) , which is what you're really interested in, right? The maximal time you reach ...

It seems there's something gone wrong beforehand; if you try psi.test_sanity() before the psi0.apply_local_op(...) , it will likely give you an error, right? If this code is inside a loop, make sure you have actual copies psi0 = psi.copy() , not just new references. That being said: in what way can ...

Sounds like you're after the bc_shift . With bc_shift=['open', -1] for finite MPS or bc_shift=['periodic', -1] for infinite MPS, couplings in "y-direction" of the lattice wind like a helix around the cylinder. More details in this userguide section . It might also be useful to look at the ...

1) Glad that this helped. Watch out for the new version of TeNPy that will be released in the next few days! 2) You're more than welcome to contribute! There's plenty of things to be done. Hmm, I'm not completely sure, but it might just be that chi=500 is too small to reach that time. I did some sim...