Crossmap
Interactive web tool for visualizing and optimizing campus coverage using grid-based optimization and comparative result views.
Explore the Project
Project Overview
The Crossmap web tool was developed as part of my role in the Crossmap research group at UMD's FIRE (First-Year Innovation and Research Experience) program. Our team implemented and expanded upon a quantum optimization paper that lacked publicly available code, building the algorithm from scratch using simulated annealing as an alternative to the quantum annealer used in the original research. You can view more of the code implementation in depth by visiting the repository here.
This web interface was my independent contribution to the research group, designed to help generate input matrices for the optimization algorithm. The interface, inspired by Photoshop's layout, allows users to place red circles with adjustable radii on campus maps, which are then exported as binary matrices (1s for marked areas, 0s for unmarked areas) for algorithm input.
Research Achievements
From-Scratch Implementation: Successfully implemented the optimization algorithm from scratch, adapting quantum annealing concepts to work with simulated annealing due to hardware limitations.
Algorithm Adaptation: Modified the original quantum approach to work with classical simulated annealing, demonstrating the flexibility of optimization techniques across different computing paradigms.
Prototype Web Tool: Developed a prototype interface for generating input matrices and visualizing campus Wi-Fi coverage patterns for future algorithm testing.
Research Impact
While limited by classical computational constraints, this work serves as a proof of concept for the scaling potential of quantum optimization as hardware matures.
The real value lies in the groundwork for future applications. Advanced quantum hardware will allow these algorithms to outperform classical computing for large-scale logistics, such as campus-wide Wi-Fi optimization and urban infrastructure design.
This from-scratch implementation validates these techniques as viable frameworks for real-world applications as hardware begins to meet theoretical requirements.