TeNPy Forum

Hi, and welcome to the forum! In Tenpy, you can compute expectation values of any operator you'd like using psi.expectation_value(). You can read the documentation here: tenpy.networks.mps.MPS

This is brilliant! I much prefer readthedocs for its native updating of the docs. Also, it brings the documentation into a format that is more consistent with other python packages (in my experience). The ads are non-intrusive but I would still recommend opting out (if there are no downsides to that...

As [mention]Johannes[/mention] mentions, the rewrite of DMRG 39 and 85 formed part of an attempt to overcome convergence issues such as described above. The crucial new feature is the introduction of single-site DMRG updates, which we hope can overcome the bad convergence. I have unfortunately not h...

First of all, the last sentence (' This you could~ smaller.') in your answer for Q1 is not clear for me. I was afraid so. Let me try again: I think you might be finding quite different states for different values of Jz. I assume you are running a separate simulation for each Jz. My suggestion would...

Hi, thanks for your questions! Firstly, if your last update was on 6th January, you might want to consider updating your code. Amongst many smaller changes, an updated version was released this April. As to your questions: Q1: At or near a critical point, DMRG can struggle with finding the correct g...

You once mentioned that 1407.6985 used an old version of tenpy, could you provide a link to the code? Thanks. Unfortunately, that version is not open source, so I cannot give you a link. There is a possibility this problem with convergence is caused by some issue deep in DMRG, which does not behave...

Problems with convergence could be due to a setup that is badly suited to DMRG. For example, in arXiv:1407.6985, convergence is bad for a weakly interacting system (which is in a metallic phase).

That said, the behaviour you report seems different from the case I mention. Did you try using a mixer?

... I have to calculate many independent ground states for different physical parameters. I was hoping I could parallelize those executions due to their independent nature. ... If you have an executable-level script (like any of the example codes) and want to run this many times in parallel for dif...

For the paper you cite, an older version of TenPy was used. The external flux was implemented by defining an additional phase factor np.exp(1j*2*np.pi*phi_ext) on one hopping term around the cylinder (i.e., collecting all flux on a single bond). This might still be the way to go in the current TenPy...

Scipy has a method for matrix exponentiation in scipy.linalg.expm.

You could potentially use the classmethod from_full() in tenpy.networks.mps.py.

I am curious why you would need a fully random MPS. Does starting from a random product state with the mixer implemented not give your desired results?

What exactly is the difference between this method and the native MPS.expectation_value()? The latter can already take in a sequence of operators.

By my (limited) understanding: compute_K() calculates the y-momentum resolved entanglement spectrum, where y is the direction along the circumference of your cylinder. This is done by obtaining the dominant eigenvector of the mixed transfer matrix found by 'rotating' part of the cylinder around the ...