TeNPy Forum

Sorry that I couldn't answer earlier, I'd been swamped with other things to finish. You're on the right track, but had some convention issue along the way: First, in TeNPy, the convention is that Jordan Wigner strings extend to the left, not to the right. The MPO you wrote down generates \sum_i \alp...

This is not supported at this point. It's also not quite trivial especially in the case of infinite MPS, since MPOs are often extensive (scaling with L), and multiplying them can give you something quadratic in L.

Thanks for the suggestion, this is indeed the way to go here. See e.g. arXiv:cond-mat/9906019 for details.

On personal laptops, I'd recommend to download anaconda and install the corresponding conda package, then you don't need any build tool yourself.
Note that the latest version 0.9 is by now over a year old, I'll release a new version either today or on Monday....

The XXZ chain by default turns on Sz conservation. Hence, there is no operator 'Sx' or 'Sy', because they don't conserve the Sz charge. Due to Sz conservation, you immediately know that expectation values of Sx and Sy are zero. See also this FAQ question However, note that the correlations <Sx_i Sx_...

The Array class has the methods drop_charge and change_charge . What's missing is functions to do this for each tensor in an MPS (or similarly, an MPO). You could do something along the following snippet: def MPS_drop_charge(psi, charge=None, chinfo=None, permute_p_leg=True): psi_c = psi.copy() psi_...

This should be fixed in the latest github version. I'll release a new version 0.10 (that can be installed with pip/conda) shortly. (Sorry, I didn't update the official releases for over a year, so there have a few fixes accumulated....)

It seems that you use Sz conservation, such that there is no operator 'Sx' or 'Sy', because they don't conserve the Sz charge. Since you model (and hence state) conserve Sz, you immediately know that expectation values of Sx and Sy are zero.
See also this FAQ question

Yes, this looks correct. The line M_tebd = models.model.NearestNeighborModel.from_MPOModel(M) is not really necessary, the Model which you have already is a NearestNeighborModel defining H_bond, so there's no need to create a new model here. In fact, you don't need to define the XXZchainnew here at ...

Indeed, the issue is that the periodic boundary conditions induce a long-range coupling in the model. (The MPS is still open, but I think you already got this.) You don't even need to define a custom model, since there is also the tenpy.models.tf_ising.TFIModel , which is the same as the TFIChain ex...

Looks like it's installed correctly, even if HAVE_MKL was not set on compile time. It's able to load the compiled parts, so no need to worry, all good! If TeNPy is compiled without HAVE_MKL, it falls back to the BLAS functioncs of numpy and scipy, and as fas as I know, numpy+scipy on Windows actuall...

If the "long range" coupling has a finite range, i.e. you put a cutoff of say 5 sites or so, and don't include any coupling afterwards, you can indeed just call add_exponentially_decaying_coupling a bunch of times with corresponding distances. If you want a "true" long range coup...

You're using a FermionChain. As the name suggests, the FermionChain has as local sites the tenpy.networks.site.FermionSite . This site doesn't define "up" or "down" states, but only "full" and "empty" states. Try to adjust the initial product state to be e.g. ...

Hi Sourav, cool, great to hear that you want to implement this in TeNPy! There's different ways to implement Lindblad evolution, so before we discuss TeNPy, we should maybe discuss a bit which way you want to implement it, and it's probably much easier to do this over zoom. I'll contact you by email...

Formally yes , you can do it by just contracting all the tensors of the MPS, leaving the physical legs open. Depending on your preferred form, you might want to combine the L physical legs of dimension d into a single, big leg of dimension dim(\mathcal{H})= d^L , but this is essentially just a resh...

In general, you should probably do this for one j in the center of your chain only and write this as C(t) = \langle \psi_0 | e^{i H t} S^z_i e^{-iHt} S^z_j | \psi_0 \rangle = \langle \psi(t) | S^z_i | \phi(t) \rangle, \quad \text{where} \quad |\psi(t) \rangle = e^{-iHt} |\psi_0 \rangle \text{~and~} ...

That's expected behaviour given the implmentation of these warnings. The engine does use the options, but only once you actually time evolve the state with eng.run() . This stems from the convention that options only need to be read out by the time when they are actually used: depending on previousl...

The issue is confusion about the (first two) arguments of correlation_function . Giving a list of operators as arguments does not mean that you evaluate correlations between multi-site terms, but that you evaluate different correlation functions on even vs odd sites: psi.correlation_function(['A','B...

Glad to hear that

I checked, and the code runs without issues on my machine. I'd guess it's some incompatibility/issue with the BLAS code being called in the end. First of all, try whether it works with export TENPY_OPTIMIZE=0 , which forces TeNPy to not load the compiled Cython parts. How did you install TeNPy? Did ...

Hi, and sorry for the slow response; I hope it's still useful, given that I added extensive examples and further functions! Let me first explain why your examples do or don't work: When you write the MPO grid as W_0 = (S^z, S^x); W_1 = (N, N^2)^T or as in the last example, The [tenpyclass=tenpy.netw...

You can also do SpIdSm = tenpy.networks.site.kron(s.Sp, s.Id, S.Sm, group=False) The tenpy.networks.site.kron function with n input onsite operators generates an operator acting on n consecutive sites at once, with n input and n output legs, e.g for n=3, p0*, p1*, p2*, p0, p1, p2 . The expectation_v...

Since you fully re-initialize the TwoSiteDMRGEngine in the example you sent without passing the environment, you actually don't reuse the environment here. You define a new (random) product_state , but don't use it to initialize psi itself, I assume you also do that within the loop? And what exactly...

Really, the proper functions is to adjust the *correlation_function methods of the MPS to the MPSEnvironment, see the new 173 . If you really just need the nearest-neighbor correlations, the expectation_value actually supports this: import tenpy s = tenpy.networks.site.SpinHalfSite() psi = tenpy.net...

The first example adds terms b^\dagger_{x,0} b_{x,1} n_{x+1,1} + b^\dagger_{x,1} b_{x,0} n_{x+1,0} , which is not hermitian due to the switch in the n index. DMRG (or more precisely the Lanczos called by DMRG) always assumes a hermitian Hamiltonian! The "poorly conditioned Lanczos" is like...