neal.sampler.SimulatedAnnealingSampler.sample

SimulatedAnnealingSampler.sample(bqm, beta_range=None, num_reads=None, num_sweeps=None, num_sweeps_per_beta=1, beta_schedule_type='geometric', seed=None, interrupt_function=None, beta_schedule=None, initial_states=None, initial_states_generator='random', **kwargs)[source]

Sample from a binary quadratic model using an implemented sample method.

Parameters:
  • bqm (dimod.BinaryQuadraticModel) – The binary quadratic model to be sampled.
  • beta_range (tuple or list, optional) – A 2-tuple or list defining the beginning and end of the beta schedule, where beta is the inverse temperature. The schedule is interpolated within this range according to the value specified by beta_schedule_type. Default range is set based on the total bias associated with each node.
  • num_reads (int, optional, default=len(initial_states) or 1) – Number of reads. Each read is generated by one run of the simulated annealing algorithm. If num_reads is not explicitly given, it is selected to match the number of initial states given. If initial states are not provided, only one read is performed.
  • num_sweeps (int, optional, default=``len(beta_schedule)*num_sweeps_per_beta`` or 1000) – Number of sweeps used in annealing. If no value is provided and beta_schedule is None the value is defaulted to 1000.
  • num_sweeps_per_beta (int, optional, default=1) – Number of sweeps to perform at each beta. One sweep consists of a sequential Metropolis update of all spins.
  • beta_schedule_type (string, optional, default="geometric") –

    Beta schedule type, or how the beta values are interpolated between the given beta_range. Supported values are:

    • ”linear”
    • ”geometric”
    • ”custom”

    ”custom” is recommended for high-performance applications, which typically require optimizing beta schedules beyond those of the “linear” and “geometric” options, with bounds beyond those provided by default. num_sweeps_per_beta and beta_schedule fully specify a custom schedule.

  • beta_schedule (array-like, optional, default = None) – Sequence of beta values swept. Format compatible with numpy.array(beta_schedule,dtype=float) required. Values should be non-negative.
  • seed (int, optional, default = None) – Seed to use for the PRNG. Specifying a particular seed with a constant set of parameters produces identical results. If not provided, a random seed is chosen.
  • initial_states (samples-like, optional, default=None) – One or more samples, each defining an initial state for all the problem variables. Initial states are given one per read, but if fewer than num_reads initial states are defined, additional values are generated as specified by initial_states_generator. See func:.as_samples for a description of “samples-like”.
  • initial_states_generator (str, "none"/"tile"/"random", optional, default="random") –

    Defines the expansion of initial_states if fewer than num_reads are specified:

    • ”none”:
      If the number of initial states specified is smaller than num_reads, raises ValueError.
    • ”tile”:
      Reuses the specified initial states if fewer than num_reads or truncates if greater.
    • ”random”:
      Expands the specified initial states with randomly generated states if fewer than num_reads or truncates if greater.
  • interrupt_function (function, optional) – If provided, interrupt_function is called with no parameters between each sample of simulated annealing. If the function returns True, then simulated annealing will terminate and return with all of the samples and energies found so far.
Returns:

dimod.SampleSet

Examples

This example runs simulated annealing on a binary quadratic model with some different input parameters.

>>> import dimod
>>> import neal
...
>>> sampler = neal.SimulatedAnnealingSampler()
>>> bqm = dimod.BinaryQuadraticModel({'a': .5, 'b': -.5},
...                                  {('a', 'b'): -1}, 0.0,
...                                  dimod.SPIN)
>>> # Run with default parameters
>>> sampleset = sampler.sample(bqm)
>>> # Run with specified parameters
>>> sampleset = sampler.sample(bqm, seed=1234,
...                            beta_range=[0.1, 4.2],
...                            num_sweeps=20,
...                            beta_schedule_type='geometric')
>>> # Reuse a seed
>>> a1 = next((sampler.sample(bqm, seed=88)).samples())['a']
>>> a2 = next((sampler.sample(bqm, seed=88)).samples())['a']
>>> a1 == a2
True