  Portfolio Optimization (PO) is a financial problem aiming to maximize the net
gains while minimizing the risks in a given investment portfolio. The novelty
of Quantum algorithms lies in their acclaimed potential and capability to solve
complex problems given the underlying Quantum Computing (QC) infrastructure.
Utilizing QC's applicable strengths to the finance industry's problems, such as
PO, allows us to solve these problems using quantum-based algorithms such as
Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization
Algorithm (QAOA). While the Quantum potential for finance is highly impactful,
the architecture and composition of the quantum circuits have not yet been
properly defined as robust financial frameworks/algorithms as state of the art
in present literature for research and design development purposes. In this
work, we propose a novel scalable framework, denoted PO-QA, to systematically
investigate the variation of quantum parameters (such as rotation blocks,
repetitions, and entanglement types) to observe their subtle effect on the
overall performance. In our paper, the performance is measured and dictated by
convergence to similar ground-state energy values for resultant optimal
solutions by each algorithm variation set for QAOA and VQE to the exact
eigensolver (classical solution). Our results provide effective insights into
comprehending PO from the lens of Quantum Machine Learning in terms of
convergence to the classical solution, which is used as a benchmark. This study
paves the way for identifying efficient configurations of quantum circuits for
solving PO and unveiling their inherent inter-relationships.
