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initialization of the EVQE population with more than one circuit layer #28

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merged 3 commits into from
May 27, 2024
Merged

initialization of the EVQE population with more than one circuit layer #28

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c927ef1
enable the initialization of the EVQE population with individuals usi…
dleidreiter Apr 29, 2024
3507d00
add changelog entry
dleidreiter May 27, 2024
263f381
Merge branch 'main' into 26-add-the-option-to-initialize-the-evqe-pop…
dleidreiter May 27, 2024
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13 changes: 12 additions & 1 deletion queasars/minimum_eigensolvers/evqe/evqe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,6 +87,11 @@ class EVQEMinimumEigensolverConfiguration:
a python ThreadPool executor. If a dask Client is used, both the Sampler and Estimator need to be serializable
by dask, otherwise the computation will fail. If no parallel_executor is provided a ThreadPoolExecutor
with as many threads as population_size will be launched
:param n_initial_layers: number of layers with which the individuals in the initial population are initialized.
By default, this is set to 1. This should only be increased, if randomize_initial_population_parameters
is set to True, or the parameter_search_probability is high. Otherwise, the added layers may start as an
identity operator and only seldom be optimized, minimizing their effect.
:type n_initial_layers: int
:param randomize_initial_population_parameters: Determines whether the parameter values of the individuals in
the first population shall be initialized randomly or at 0. By default, the parameter values in the
initial population are initialized randomly
Expand Down Expand Up @@ -116,6 +121,7 @@ class EVQEMinimumEigensolverConfiguration:
parameter_search_probability: float
topological_search_probability: float
layer_removal_probability: float
n_initial_layers: int = 1
randomize_initial_population_parameters: bool = True
parallel_executor: Union[Client, ThreadPoolExecutor, None] = None
mutually_exclusive_primitives: bool = True
Expand All @@ -132,6 +138,11 @@ def __post_init__(self):
raise ValueError("The topological_search_probability must not exceed the range (0, 1)!")
if not 0 <= self.layer_removal_probability <= 1:
raise ValueError("The layer_removal_probability must not exceed the range (0, 1)!")
if self.n_initial_layers < 1:
raise ValueError(
"The number of initial layers for each individual "
+ f"of the population must be at least 1! But it was {self.n_initial_layers}!"
)


class EVQEMinimumEigensolver(EvolvingAnsatzMinimumEigensolver):
Expand All @@ -146,7 +157,7 @@ def __init__(self, configuration: EVQEMinimumEigensolverConfiguration):

population_initializer: Callable[[int], EVQEPopulation] = lambda n_qubits: EVQEPopulation.random_population(
n_qubits=n_qubits,
n_layers=1,
n_layers=configuration.n_initial_layers,
n_individuals=configuration.population_size,
randomize_parameter_values=configuration.randomize_initial_population_parameters,
random_seed=new_random_seed(random_generator=self.random_generator),
Expand Down
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