Quantum Global Minimum Finder based on Variational Quantum Search

TL;DR

This paper introduces the Quantum Global Minimum Finder (QGMF), a quantum algorithm designed to efficiently locate global minima in complex non-convex functions, optimized for NISQ devices and leveraging binary search and variational quantum search techniques.

Contribution

The paper presents a novel quantum algorithm, QGMF, combining binary search and variational quantum search, optimized for NISQ devices, to improve global optimization of non-convex functions.

Findings

QGMF effectively finds global minima in non-convex functions.

QGMF is optimized for NISQ hardware with low-depth circuits.

Binary search enhances scalability and efficiency.

Abstract

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.