class netket.driver.SteadyState(lindbladian, optimizer, *args, variational_state=None, sr=None, sr_restart=False, **kwargs)[source]¶

Bases: netket.driver.abstract_variational_driver.AbstractVariationalDriver

Steady-state driver minimizing L^†L.

__init__(lindbladian, optimizer, *args, variational_state=None, sr=None, sr_restart=False, **kwargs)[source]¶

Initializes the driver class.

  • lindbladian – The Lindbladian of the system.

  • optimizer – Determines how optimization steps are performed given the bare energy gradient.

  • sr – Determines whether and how stochastic reconfiguration is applied to the bare energy gradient before performing applying the optimizer. If this parameter is not passed or None, SR is not used.

  • sr_restart – whever to restart the SR solver at every iteration, or use the previous result to speed it up


Return MCMC statistics for the expectation value of observables in the current state of the driver.


Returns the machine that is optimized by this driver.


Returns a monotonic integer labelling all the steps performed by this driver. This can be used, for example, to identify the line in a log file.


Performs steps optimization steps.

steps: (Default=1) number of steps


steps (int) –


Return MCMC statistics for the expectation value of observables in the current state of the driver.


observables – A pytree of operators for which statistics should be computed.


A pytree of the same structure as the input, containing MCMC statistics for the corresponding operators as leaves.


Returns an info string used to print information to screen about this driver.

iter(n_steps, step=1)¶

Returns a generator which advances the VMC optimization, yielding after every step_size steps.

  • n_iter – The total number of steps to perform.

  • step_size – The number of internal steps the simulation is advanced every turn.

  • n_steps (int) –

  • step (int) –


int – The current step.


Resets the driver. Concrete drivers should also call super().reset() to ensure that the step count is set to 0.

run(n_iter, out=None, obs=None, show_progress=True, save_params_every=50, write_every=50, step_size=1, callback=<function AbstractVariationalDriver.<lambda>>)¶

Executes the Monte Carlo Variational optimization, updating the weights of the network stored in this driver for n_iter steps and dumping values of the observables obs in the output logger. If no logger is specified, creates a json file at out, overwriting files with the same prefix.

By default uses netket.logging.JsonLog. To know about the output format check it’s documentation. The logger object is also returned at the end of this function so that you can inspect the results without reading the json output.

  • n_iter – the total number of iterations

  • out – A logger object, or an iterable of loggers, to be used to store simulation log and data. If this argument is a string, it will be used as output prefix for the standard JSON logger.

  • obs – An iterable containing all observables that should be computed

  • save_params_every – Every how many steps the parameters of the network should be serialized to disk (ignored if logger is provided)

  • write_every – Every how many steps the json data should be flushed to disk (ignored if logger is provided)

  • step_size – Every how many steps should observables be logged to disk (default=1)

  • show_progress – If true displays a progress bar (default=True)

  • callback – Callable or list of callable callback functions to stop training given a condition


Updates the parameters of the machine using the optimizer in this driver


dp – the pytree containing the updates to the parameters