dwave.system.temperatures.fast_effective_temperature#

fast_effective_temperature(sampler: Sampler, num_reads: int | None = None, seed: None | int | RandomState = None, h_range: Tuple = (-0.1639344262295082, 0.1639344262295082), nodelist: List | None = None, sampler_params: dict | None = None, optimize_method: str | None = 'bisect', num_bootstrap_samples: int | None = 0) → Tuple[float64, float64][source]#

Provides an estimate to the effective temperature, \(T\), of a sampler.

This function submits a set of single-qubit problems to a sampler and uses the rate of excitations to infer a maximum-likelihood estimate of temperature. For greater control of the problem Hamiltonian or more general samplers, maximum_pseudolikelihood_temperature should be preferred.

Parameters:

  • sampler (dimod.Sampler, optional, default=DWaveSampler) – A dimod sampler.

  • num_reads (int, optional) – Number of reads to use. Default is 100 if not specified in sampler_params.

  • seed (int, optional) – Seeds the problem generation process. Allowing reproducibility from pseudo-random samplers.

  • h_range (float, default = [-1/6.1,1/6.1]) – Determines the range of external fields probed for temperature inference. Default is based on a D-Wave Advantage processor, where single-qubit freeze-out implies an effective temperature of 6.1 (see freezeout_effective_temperature). The range should be chosen inversely proportional to the anticipated temperature for statistical efficiency, and to accomodate precision and other nonidealities such as precision limitations.

  • nodelist (list, optional) – Variables included in the estimator. If not provided defaulted to the nodelist of the structured sampler (QPU) specified.

  • sampler_params (dict, optional) – Any additional non-defaulted sampler parameterization. If num_reads is a key, must be compatible with num_reads argument.

  • optimize_method (str, optional) – Optimize method used by SciPy root_scalar method. The default method works well under default operation, ‘bisect’ can be numerically more stable when operated without defaults.

  • num_bootstrap_samples (int, optional, default=0) – Number of bootstrap samples to use for estimation of the standard error. By default no bootstrapping is performed and the standard error is defaulted to 0.

Returns:

The effective temperature describing single qubit problems in an external field, and a standard error (+/- 1 sigma). By default the confidence interval is set as 0.

Return type:

Tuple[float, float]

Raises:

  • ValueError – If the sampler is not structured, and no nodelist is provided.

  • ValueError – If num_reads is inconsistent with sampler_params

See also

https://doi.org/10.3389/fict.2016.00023

https://www.jstor.org/stable/25464568

Examples

Draw samples from a DWaveSampler and establish the temperature

>>> from dwave.system.temperatures import fast_effective_temperature
>>> from dwave.system import DWaveSampler
>>> sampler = DWaveSampler()
>>> T, _ = fast_effective_temperature(sampler)
>>> print('Effective temperature at freeze-out is',T)    
Effective temperature at freeze-out is  0.21685104745347336

See also

The function freezeout_effective_temperature may be used in combination with published device values to estimate single-qubit freeze-out, in approximate agreement with empirical estimates of this function.

https://doi.org/10.3389/fict.2016.00023

https://www.jstor.org/stable/25464568