  We consider digitized-counterdiabatic quantum computing as an advanced
paradigm to approach quantum advantage for industrial applications in the NISQ
era. We apply this concept to investigate a discrete mean-variance portfolio
optimization problem, showing its usefulness in a key finance application. Our
analysis shows a drastic improvement in the success probabilities of the
resulting digital quantum algorithm when approximate counterdiabatic techniques
are introduced. Along these lines, we discuss the enhanced performance of our
methods over variational quantum algorithms like QAOA and DC-QAOA.
