  The search for global minima is a critical challenge across multiple fields
including engineering, finance, and artificial intelligence, particularly with
non-convex functions that feature multiple local optima, complicating
optimization efforts. We introduce the Quantum Global Minimum Finder (QGMF), an
innovative quantum computing approach that efficiently identifies global
minima. QGMF combines binary search techniques to shift the objective function
to a suitable position and then employs Variational Quantum Search to precisely
locate the global minimum within this targeted subspace. Designed with a
low-depth circuit architecture, QGMF is optimized for Noisy Intermediate-Scale
Quantum (NISQ) devices, utilizing the logarithmic benefits of binary search to
enhance scalability and efficiency. This work demonstrates the impact of QGMF
in advancing the capabilities of quantum computing to overcome complex
non-convex optimization challenges effectively.
