TeNPy Forum

The model is gapless in the range -1 < V < 1, so this is definitely a difficult region. As far as I know, VUMPS might indeed be better suited for that, feel free to implement it ;-) Which version of tenpy are you using? In particular, does your version include the fix of 95 (v0.5.0 or later)? The is...

By definition, a tenpy.models.model.NearestNeighborModel can only contain nearest-neighbor couplings. That is the case because it provides a representation of the hamiltonian in terms of H_bond , where each H_bond is a term involving just two neighboring sites. Hence, you can *not* make a model inhe...

Readthedocs currently includes advertisement, but we could opt-out of that.

Hi all, so far, I had hosted the documentation on https://tenpy.github.io with github pages. I have now tried to setup the documentation on https://tenpy.readthedocs.io as well, which is an awesome place hosting documentation for many, many python packages (and other stuff as well). The nice thing t...

Version 0.5.0 has been released. Before updating , please read the corresponding section in the change log , in particular the part on the backwards incompatible changes! It is now supported to install TeNPy with pip via pip install --upgrade physics-tenpy # for 'stable' versions I'd recommend an in...

Welcome! I think the problem depends on how you installed tenpy. Did you pip install physics-tenpy , or did you download the code and use inline]python setup.py install[/inline] from within the TeNPy folder, or did you simply set export PYTHONPATH=/Users/username/TeNPy" ? In the latter case, it...

If you run the code with conserve='N' , you should get a UserWarning tenpy/networks/mps.py:396: UserWarning: flat array has non-zero entries in blocks incompatible with charge B = npc.Array.from_ndarray(B, legs, dtype) The reason is that for conserve='N' , each tensor needs to have a well defined ch...

You're absolutely right, the different type of "Site" makes the difference here.
That's certainly a thing to keep in mind, so I actually added your example (slightly extended) to the documentation of from_product_state.
Do you agree that it is more clear now?

Yes, that is indeed what you want. Just for reference, here is the documentation: from_product_state . If you look at the example, one line below the state is normalized, such that it gets np.array([1/sqrt(2), -1/sqrt(2)]) . This is the negative-x state of a spin-1/2 and equivalent to the np.array([...

So, is it fine to miss S? or is it typo? That should be fine, it's just the difference between checking the absolute and the relative error. For the energy, we need to check the relative error: the energy scale can vary by quite a bit simply by rescaling your Hamiltonian. However, the entropy can n...

The all-up state is an exact eigenstate of the model and hence won't evolve in time, in particular if you use Sz-conservation (which you do in this case): the all-up state is the only state in the sector with S_{total} = L/2 . Try starting from a different state, e.g. all up but one spin down (which...

Yes, that's for sure a good idea. Indeed, I thought about it and started to implement it already a few days ago, but I'm quite busy these days (moving to UC Berkeley ), so I didn't have time to finish it. Hopefully, I can find a bit time next week - unless you volunteer to time to implement it

One alternative is to iteratively do QR decompositions (and RQ decompositions). You decompose your state as M M M M M = Q R M M M M = Q Q R M M M = ... = Q Q Q Q R and as M M M M M = M M M M R' Q' = M M M R' Q' Q' = ... = R' Q' Q' Q' Q' until your iterations converge. Then your state is in the form ...

What order (second or fourth) and timestep do you use for TEBD? Does this go away if you simply use a smaller timestep/higher order? Did you try to look at the overlap with the initial ground state returned by DMRG (as a function of time)? Converging just 3 digits in energy still a bit unprecise, ma...

Sorry, that was a bug in MPS.get_rho_segment. It should be fixed with the latest version.

In general, the parameter N_sweeps_check should not change the convergence properties - at least not much.... Let's look at what it influences: For checking the convergence, DMRG checks the average change of the energy / entropy per sweep in the last N_sweeps_check steps. The parameter update_env de...

The function entanglement_entropy_segment should accept disconnected segments, e.g. segment=[0, 2, 4, 6, 8] . However, it works only for *small* enough number of sites in your density matrix - it's exponentially expensive, because it constructs the full density matrix, tracing out the rest of the si...

Hi, indeed, the irregular lattice is still an open 77 . Sorry, there's so much stuff to do. I had to write and defend my PHD thesis, so I didn't have too much time to work on TeNPy lately. Hopefully, this will change again. ;) To make sure I understand correctly, you want a geometry like this: Boson...

is there a way to have a finite periodic chain (so a ring) for the BoseHubbard model? Kind of. We do not support finite MPS with periodic boundaries for the MPS itself i.e. writing | \Psi\rangle = \sum Tr(A^{\sigma_1} \cdots A^{\sigma_L}) | \sigma_1 \cdots \sigma_L\rangle This is a form which is us...

I've added the `flush=True` to the print statements during the loops of TEBD and DMRG in ce32fd1fc30073918371717beea110083bab8cd1 On my workstation, the flush causes the output to appear immediately. If you redirect to a file on a network-shared file system, there might be a delay of the change bein...

Welcome! There is a way to add such couplings, namly with add_multi_coupling of the tenpy.models.model.MultiCouplingModel instead of just the add_coupling of the tenpy.models.model.CouplingModel . The tenpy.models.toric_code.ToricCode is an example where this is used. Take a look at this topic for m...

There's also a option to print().

Still, there are ways around this. You're describing the problem that the file is "buffered", which Linux/Python does automatically to avoid unnecessary hard disk usage slowing down the execution of the program. However, this can be switched off. For python, try to use the -u option for th...

A such enormous correlation length \xi > 10^10 means that the second eigenvalue of the transfer matrix is 1 up to numerical errors. The reason is that the state you get is a cat state, i.e. a superposition of all |+x \rangle and all |-x \rangle states. See also the discussion in viewtopic.php?f=7&am...

Welcome Dhiman, and sorry for not answering earlier. You might want to take a look at the tenpy.models.haldane.FermionicHaldaneModel and the tenpy.models.haldane.BosonicHaldaneModel . Both are based on the Hamiltonian from the paper 1407.6985 discussing the fermionic model. As far as I see from a qu...