Portfolio optimization with digitized counterdiabatic quantum algorithms

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