GravOptAdaptiveE is a sophisticated quantum-inspired optimization algorithm designed to solve the MAX-CUT problem with remarkable efficiency. This project showcases the potential of quantum computing techniques in addressing complex optimization challenges by achieving an impressive 89.17% cut in under 9 seconds.

Key features of the GravOptAdaptiveE algorithm include:

• Quantum-Inspired Techniques: Utilizing principles inspired by quantum mechanics to enhance optimization processes.
• Rapid Performance: The algorithm demonstrates the ability to produce high-quality solutions quickly, with significant time savings compared to traditional methods.
• Scalability: Designed to tackle large-scale optimization problems effectively.

For practical implementations, users can leverage the algorithm to address various optimization challenges across different fields, including operations research and computational biology.

Example Usage

To use the GravOptAdaptiveE algorithm, implement it in the following manner:

result = grav_opt_adaptive_e(input_data)
print("Optimal Cut: ", result)

GravOptAdaptiveE not only showcases innovative computational approaches but also serves as a valuable resource for researchers and practitioners looking to explore the intersection of quantum theory and optimization.

GitHub: https://github.com/Kretski/GravOptAdaptiveE X thread: https://x.com/DKretski/status/1990560176450027524 Preprint: https://vixra.org/abs/2511.17607773