  Quantum computers are gaining importance in various applications like quantum
machine learning and quantum signal processing. These applications face
significant challenges in loading classical datasets into quantum memory. With
numerous algorithms available and multiple quality attributes to consider,
comparing data loading methods is complex.
  Our objective is to compare (in a structured manner) various algorithms for
loading classical datasets into quantum memory (by converting statevectors to
circuits).
  We evaluate state preparation algorithms based on five key attributes:
circuit depth, qubit count, classical runtime, statevector representation
(dense or sparse), and circuit alterability. We use the Pareto set as a
multi-objective optimization tool to identify algorithms with the best
combination of properties. To improve comprehension and speed up comparisons,
we also visually compare three metrics (namely, circuit depth, qubit count, and
classical runtime).
  We compare seven algorithms for dense statevector conversion and six for
sparse statevector conversion. Our analysis reduces the initial set of
algorithms to two dense and two sparse groups, highlighting inherent
trade-offs.
  This comparison methodology offers a structured approach for selecting
algorithms based on specific needs. Researchers and practitioners can use it to
help select data-loading algorithms for various quantum computing tasks.
