Measurements for Simulations¶
When we run a simulation performing a time evolution, we are interested in measurements
after every (n-th) time step, but it would be too costly (in terms of disk space) to save the
full psi at each time step; we only have the
|psi(t)> during the simulation, not afterwards.
Hence, we need to define what measurements we want to perform for a given simulation before
For variational ground state searches, e.g. DMRG, the situation is better: we’re not interested in how we got to the ground state, but only properties of the ground state itselft. In this case, we can first run DMRG, save the state, and then perform additional measurements and analysis after finishing the simulation, so it is not crucial to define all the measurements before the simulation.
The setup for simulations in TeNPy is as follows.
For each measurement that is to be done, we need a measurement function that evaluates whatever we want to measure, e.g., the expectation value or correlation function of some operators. If needed, you can define your own, custom functions.
For a given simulation, we specify the list of measurement functions in the simulation parameter
When the simulation runs, it calls the
make_measurements()method each time a set of measurements should be performed, e.g. on the initial state, during the time evolution, and on the final state. This causes a call to each of the measurement functions specified in the
Simulation.connect_measurementsparameter, passing the current state
psi, model, simulationas arguments (possibly amongst other keyword arguments also specified in
Simulation.connect_measurements). Moreover, it passes a dictionary
results, in which measurement results should be saved. At the end of make_measurements, the simulation class merges the obtained results into the collection
resultsof all previous measurements
At the end of simulation, the results are saved and returned for further analysis (e.g. plotting).
In the simplest case, a measurement function is just a function, which can take the keyword arguments
results, psi, model, simulation and saves the measurement results in the dictionary results.
The other arguments psi and model are the current MPS and model that can be used for measurements,
and simulation gives access to the full simulation class, in case other addiontal data is needed.
Within TeNPy, we use the convention that measurement functions (taking these arguments and saving to results instead
of simply returning values) start with an
m_ in their name.
A few generic measurement functions are defined in
As a first, somewhat trivial example, let us look at the source code of
def m_entropy(results, psi, model, simulation, results_key='entropy'): results[results_key] = psi.entanglement_entropy()
As you can see, it’s a simple wrapper around the MPS method
Note that usually the psi and model arguments are the same as the simulation attributes
simulation.model, but they can be different in certain cases, e.g. when grouping sites.
In most cases, you should directly use the passed psi and model.
Of course, you can also do some actual calculations in the measurement functions.
A good example of this is the
tenpy.simulations.measurement.m_onsite_expectation_value() - take a look at it’s
source code. Another example could be the m_pollmann_turner_inversion measurement function defined in the
model_custom.py example from the Simulations guide.
The connect_measurements parameter¶
Simulation.connect_measurements parameter is a list with one entry for each measurment function to be
used. Each function is specified by a tuple
module, func_name, extra_kwargs, priority.
Here, module and func specfiy the module and name of the function, extra_kwargs are (optional) additional keyword
arguments to be given to the function, and priority allows to control the order in which the measurement functions get
called. The latter is usefull if you want to “post-process” results of another measurement function.
For example, say you want to measure local expectation values of both Sz and Sx with
tenpy.simulations.measurment.m_onsite_expectation_value(), then you could use
connect_measurements: - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sx - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sz
These measurement functions have default results_key under which they save values in the results, so you can then
results['<Sz>'] in the simulation results.
If you want other keys, you can explicitly specify them with the results_key argument of the function, e.g.,
connect_measurements: - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sx results_key: X_i # save as results['X_i'] - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sz results_key: Z_i # save as results['Z_i']
Some measurements are actually that common that they get added by default to the simulations (unless you explicitly
disable them with
Simulation.use_default_measurements); for example the
is measured for any simulation, as it appears in
Often, what you want to measure is just calling a method of the state psi, so there is a special syntax in the
if you specify the first entry to be
simulation_method, you can call a method of the
As for global measurement functions, we pass the corresponding
results, psi, model, simulation keyword arguments,
e.g. psi_method measurement functions need to accept
results, model, simulation as arguments, and
simulation_method measurement functions should accept
results, psi, model.
This is already very usefull to call measurement functions defined inside (custom) models or simulation classes,
yet methods of psi don’t follow the measurement function call structure, but simply return values.
For those cases, you can use another special syntax, namely to simply add `wrap` before the function name.
In this case, we don’t pass
results, psi, model, simulation, but simply save the return values of the function
in the results, under the results_key that gets passed as extra keyword argument,
see (the source code of)
The results_key defaults to the function name.
To make this clearer, let’s extend the example above with more measurements:
connect_measurements: - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sx - - tenpy.simulations.measurement - m_onsite_expectation_value - opname: Sz - - psi_method - wrap correlation_function # call psi.correlation_function() - results_key: '<Sz Sz>' # save returned value as results["<Sz Sz>"] ops1: Sz # other (necessary) arguments to psi.correlation_function ops2: Sz - - simulation_method - wrap walltime # "measure" wall clock time it took to run so far - - tenpy.tools.process - wrap memory_usage # "measure" the current RAM usage in MB