MagicRounding#

class MagicRounding(sampler, basis_sampling='uniform', seed=None)[source]#

Bases: RoundingScheme

Magic rounding scheme that measures in magic bases, and then uses the measurement results to round the solution. Since the magic rounding is based on the measurement results, it requires a quantum backend, which can be either hardware or a simulator.

The details are described in https://arxiv.org/abs/2111.03167.

Parameters:

sampler (BaseSampler) – Sampler to use for sampling the magic bases.
basis_sampling (str) – Method to use for sampling the magic bases. Must be either "uniform" (default) or "weighted". "uniform" samples all magic bases uniformly, and is the method described in https://arxiv.org/abs/2111.03167. "weighted" attempts to choose bases strategically using the Pauli expectation values from the minimum eigensolver. However, the approximation bounds given in https://arxiv.org/abs/2111.03167 apply only to "uniform" sampling.
seed (int | None) – Seed for random number generator, which is used to sample the magic bases.

Raises:

ValueError – If basis_sampling is not "uniform" or "weighted".
ValueError – If the sampler is not configured with a number of shots.

Attributes

basis_sampling#: Basis sampling method (either "uniform" or "weighted").

sampler#: Returns the Sampler used to sample the magic bases.

Methods

round(rounding_context)[source]#

Perform magic rounding using the given RoundingContext.

Parameters:

rounding_context (RoundingContext) – The context containing the information needed for the rounding.

Returns:

The results of the magic rounding process.

Return type:

RoundingResult

Raises:

ValueError – If the rounding context has no circuits.
ValueError – If the rounding context has no expectation values for magic rounding with the weighted sampling.
QiskitOptimizationError – If the magic rounding did not return the expected number of shots.
QiskitOptimizationError – If the magic rounding did not return the expected number of bases.