QOPF - A Quantum Optimum-Path Forest Framework for Optimization and Machine Learning Problems
Abstract
The Optimum-Path Forest (OPF) is a graph-based framework developed by our research group that spans several research fields, from optimization to machine learning. OPF has also been used for image filtering and computer vision, e.g., object tracking. The framework generalizes the well-known Dijkstra approach for multiple source nodes and smooth path-cost functions. OPF can also compute minimum spanning trees over graphs and shortest paths among all nodes, like the famous Floyd-Warshall algorithm. The well-known traveling salesman problem is another example OPF can address without too many modifications. The range of applications the framework deliversis vast, and its extension to the quantum computing field seems logical. This proposal aims at designing the QOPF, i.e., a Quantum Optimum-Path Forest framework that incorporates the background of quantum physics into computing models. We will investigate its direct applications to optimization problems and artificial intelligence, establishing the roots for future work on graph-based quantumalgorithms. This proposal relates to the following S&T budget categories: (i) basic and (ii) applied research. Concerning the ONR sScience of Autonomy Program, this proposal relates to the "perception and intelligent decision making" area. Lastly, Mr. Kyle Gustafson is the ONR Program Officer collaborator to this proposal.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 08, 2024
- Source ID
- N629092412012
Entities
People
- Joao Papa
Organizations
- Office of Naval Research
- United States Navy