Hi Joseph, When you conserve Sz, you should include both 'CONSERVED_QUANTUMNUMBERS' : 'Sz' and the actual value of the magnetization desired, e.g. 'Sz' : 0.0 in the parms data. If you do not put any value for Sz then tebd will work in the sector with all spins up, as you described. -Michael Hello all again,

I have been having a problem with my results for quenching a spin-1/2 XXZ model in the TEBD code and both my colleagues and I are stumped. I am attempting to study a quench from Delta = 4 to 2 in the XXZ model, starting from the ground state of the original Hamiltonian, and looking at the local magnetisation and correlation functions that result. I tried this out in small systems (around L=32), following the tutorials very closely and adapting them to my situation, and looking at the local magnetisation. When I included S_z (the total S_z) as a conserved quantum number in my parameters, I got no time dependence in my results, with each S_z being 1/2 everywhere along the chain for every time, which seems to be nonsensical. However, when I removed this constraint, I got (after a much longer simulation) more sensible results, with a time-dependence and the results being closer to 0. I am fairly certain that S_z should be conserved here, so my question is why does this not seem to work? I followed the tutorials very closely, and those scripts seem to work well enough. The parameter section of my script is as follows:

import pyalps import matplotlib.pyplot as plt import pyalps.plot import numpy as np import copy

parms = [ { 'L' : 32, 'MODEL' : 'spin', 'local_S' : 0.5, 'Jxy' : 1, 'Jz' : 4.0, 'ITP_CHIS' : [40, 40, 40], 'ITP_DTS' : [0.05, 0.05, 0.025], 'ITP_CONVS' : [1E-9, 1E-9, 1E-10], 'INITIAL_STATE' : 'ground', 'CHI_LIMIT' : 40, 'TRUNC_LIMIT' : 1E-12, 'NUM_THREADS' : 1, 'TAUS' : [0.0, 50.0], 'POWS' : [0.0, 1.0], 'GS' : ['Jz', 'Jz'], 'GIS' : [4.0, 2.0], 'GFS' : [2.0, 2.0], 'NUMSTEPS' : [100, 2500], 'STEPSFORSTORE' : [5, 5] } ]

baseName = 'Jz4to2Quench1L32' nmlname = pyalps.writeTEBDfiles(parms, baseName) res = pyalps.runTEBD(nmlname)

Data = pyalps.load.loadTimeEvolution(pyalps.getResultFiles(prefix = 'Jz4to2Quench1L32'), measurements = ['Local Magnetization'])

Any help would be greatly appreciated, as my simulations would be much faster if I could include this!

Thanks,

Joseph Prentice