AggregationStrategy

class AggregationStrategy

Bases: ABC

Combines per-partition candidates into scored global solutions.

Subclasses implement aggregate(), which receives the executed programs plus the problem-specific hooks needed to build and score global solutions, and returns the top-N (score, solution) pairs.

Methods Summary

aggregate(programs, initial_solution, ...[, ...])

Aggregate per-partition candidates into the top-N global solutions.

Methods Documentation

abstractmethod aggregate(programs, initial_solution, extend_fn, evaluate_fn, top_n=1)

Aggregate per-partition candidates into the top-N global solutions.

Parameters:

• programs (dict[Any, SolutionSamplingMixin]) – Mapping of program IDs to executed VariationalQuantumAlgorithm instances.

• initial_solution (Sequence[int]) – Starting global solution vector (typically all zeros).

• extend_fn (Callable[[list[int], Any, SolutionEntry], list[int]]) – (current_solution, prog_id, candidate) -> extended_solution; splices a partition’s candidate into the global vector.

• evaluate_fn (Callable[[list[int]], float]) – (solution) -> float. Lower is better.

• top_n (int) – Number of top solutions to return.

Return type:

list[tuple[float, list[int]]]

Returns:

List of (score, solution) tuples sorted ascending by score (best first), with at most top_n entries.