OptiQAP

OptiQAP is a research-oriented solver toolkit for the Quadratic Assignment Problem (QAP), an NP-hard combinatorial optimization problem that appears in layout, assignment, and mapping tasks. The canonical repository is alawein/optiqap (formerly qaplibria/qap-solver), deployed at optiqap.online. The project carries a P2 priority and is tagged with quantum-computing, optimization, HPC, and computational-physics.

In a quantum-computing context, QAP-style formulations are commonly used for qubit-to-hardware mapping and constraint-aware placement, making this project a natural bridge between combinatorial optimization research and near-term quantum systems. This aligns with the broader research profile of Meshal Alawein, a computational physicist whose work spans quantum computing, high-performance physics simulations, and AI governance.

The library implements 31 methods across three families: equilibrium-guided optimization (EGO), spectral methods, and QUBO/simulated annealing. Built in Python 3.10+ with NumPy/SciPy and Makefile-driven workflows. A static landing page is served via the website/ + vercel.json pattern.

Note: The Python package is still named qaplibria internally (intentional — see workspace CLAUDE.md). Imports remain from qaplibria import ...; the repo, CLI, and brand are optiqap.