TeNPy Forum

Your code looks right. I would have done it basically the same way :) In the newest version of TeNPy, tenpy.networks.site.multi_sites_combine_charges is deprecated in favor of tenpy.networks.site.set_common_charges , you should call it with set_common_charges([siteA, siteB], 'independent') . This sh...

the line states = dmrg_params['orthogonal_to'] = [] creates a new list and assigns it to both the variable name states and dmrg_params['orthogonal_to'] . Remember, in Python everything is an "object", and your variables really are just references to objects. Inside the loop, you first crea...

1) How did you try to install TeNPy? It looks like it's using a version installed with `pip` into the local home directory. Can you try to pip uninstall physics-tenpy and then try to re-install TeNPy? Moreover, the svd_gesvd hints that you're using a very old version of scipy? What python are you us...

1) That was intentional, you need a new copy each time in the loop! After all, you want to have a list of 3 different states in the end. You might at most argue that it's a bad idea to "restart" the whole DMRG from scratch for the excited states, if we already found a ground state. But the...

This wasn't implemented yet in TeNPy , as you have probably seen by the ToDo entry. However, since today, it is: 64cd7a3a690e80b2876fce48e79675f060b52ab7 ;) The function from_infiniteT_canonical gives you an initial density matrix with fixed particle number on the physical sector, i.e. the canonical...

Welcome! This forum is meant to be a community place for both the users and developers of the Tensor Network Python (TeNPy) package. The source code of this package is available on github . The official documentation (generated from the latest source code) has also a user-guide section with some tu...

Even though the model is originially nearest-neighbor on the periodic chain, it will have longer-range interactions in the (open-boundary) MPS - with default ordering, there is one interaction going from site 0 to site L-1, or if you use the "folded" order, most of the originally nearest-n...

That's why the warning is there - to help you spot typos

Well, TEBD is nice because it is conceptually very simple - you just use a Suzuki-Trotter decomposition and straight forward iterations of (apply two site gate; compress & split to get back to MPS form). Grouping sites makes things more costly, however - formally TEBD scales as \mathcal{O}(\chi^...

First of all, let me clarify: Do you want some uniform "background density" and then some more particles in the center? Or do you only want particles in the center? For 1): If you don't enforce charge conservation, DMRG is "allowed" to switch to another charge sector, even if you...

you need to make a loop and run DMRG multiple times, each time orthogonalizing against the previous state: psi0 = MPS.from_product_state(...) states = dmrg_params['orthogonal_to'] = [] for i in range(3): psi = psi0.copy() results = dmrg.run(psi, M, dmrg_params) states.append(psi) # this also adds th...

Let's look at the example Ladder image: https://tenpy.readthedocs.io/en/latest/reference/tenpy-models-lattice-Ladder-1.png The MPS follows the red dashed line 0 - 1 - 2 -3 -4 -5- ... When you cut the MPS at the bond between 3 and 4, it will split the system in regions left = {0, 1, 2, 3} and right={...

If you have at most next-nearest neighbor interactions in a 1D chain, you can group sites and then perform TEBD. In general, I'd recommend to switch to another time evolution algorithm, though: either TDVP or the tenpy.algorithms.mpo_evolution.ExpMPOEvolution can deal with longer range. The latter e...

Welcome, Rike!

chi_min can only avoid reducing the bond dimension, not increase it further.
In addition to setting chi_min to the same value as chi_max, you should set svd_min to 0.

Clearly, you can only do this for small system sizes, where you can also do a full exact diagonalization. DMRG really is not a good algorithm at finding excited states, as I explained in the earlier posting You should just use exact diagonalization to find (all) the excited states, and then convert ...

I'm not sure what exactly you mean with "occupation-number-representation basis". You have a local basis of occupations, e.g. for spin-less fermions just |0\rangle, |1\rangle> = c^\dagger |0\rangle , or for spin-full fermions |0\rangle , |\uparrow\rangle \equiv c^\dagger_\uparrow|0\rangle,...

You tried to find *all* possible eigenstates, right? Really, DMRG is not good at finding highly excited states - that's not what it is designed for ;) What fails is not the calculation of the energy, but the trick of finding excited eigenstates with an algorithm (DMRG) looking for the ground state. ...

Sorry for the late reply; your post was displayed on another page that I didn't see. To see how you can use Sz conservation, take a look at the implementation of the tenpy.models.xxz_chain.XXZChain2 The lines self.add_coupling(Jxx * 0.5, u1, 'Sp', u2, 'Sm', dx, plus_hc=True) self.add_coupling(Jz, u1...

Wow, I'm even mentioned in the acknowledgement

Thanks for coming back on this! It's great to see what people can do with TeNPy.
I've right away updated the list of papers citing TeNPy, your paper is now included

Given that you talked about charges, I'll assume that you try to conserve the total Sz spin. In this case, the all-up state and the Neel state are in completely different sectors, and you can not add them up in a single MPS! Instead, you can and should run DMRG independently in each of these sectors...

There is a reason that MPS.add doesn't support adding states of infinite MPS: usually, you really want to avoid writing infinte MPS cat states. I would advice against using it as an initial state for iDMRG... If you do that, you just try to find two ground states at the same time. DMRG should at som...

Hi Pasa, from_product_state and from_singlets call that function in the end. The source code of from_Bflat might serve as an example. The first argument really is just a list of the B tensor that make up the MPS, in the form of numpy arrays. The topic title indicates that you want to write a cat sta...

DMRG scales with (at least) the third power of the local dimension. Using a GroupedSite forces the local dimension to 2^3 = 8, so you can expect a very significant speedup of roughly \frac{8^3}{3\cdot 2^3} when switching to separate sites. To implement it, simply use a lattice with a non-trivial uni...