Thomas Gabor
ABSTRACT
We discuss a small study on how to compare the performance of various solving techniques for quadratic unconstrained binary optimization (QUBO). Since well-known metrics are seldomly applicable, we suggest comparing the relative performance, i.e., how much the quality of solution (compared to other solutions of the same solver) for a QUBO shifts between different solving techniques. We propose looking for big shifts systematically for an empirical complexity analysis.
Code is available at github.com/thomasgabor/gecco-relative.
- Nike Dattani, Szilard Szalay, and Nick Chancellor. 2019. Pegasus: The second connectivity graph for large-scale quantum annealing hardware. arXiv preprint arXiv:1901.07636 (2019).Google Scholar
- Thomas Gabor, Michael Lachner, Nico Kraus, Christoph Roch, Jonas Stein, Daniel Ratke, and Claudia Linnhoff-Popien. 2022. Modifying the quantum-assisted genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2205--2213.Google ScholarDigital Library
- Scott Kirkpatrick, C Daniel Gelatt Jr, and Mario P Vecchi. 1983. Optimization by simulated annealing. science 220, 4598 (1983), 671--680.Google Scholar
- Andrew Lucas. 2014. Ising formulations of many NP problems. Frontiers in physics 2 (2014), 5.Google Scholar
- Gintaras Palubeckis. 2004. Multistart tabu search strategies for the unconstrained binary quadratic optimization problem. Annals of Operations Research 131 (2004), 259--282.Google ScholarCross Ref
Index Terms
- A Relative Approach to Comparative Performance Analysis for Quantum Optimization
Recommendations
Investigating the performance of an adiabatic quantum optimization processor
Adiabatic quantum optimization offers a new method for solving hard optimization problems. In this paper we calculate median adiabatic times (in seconds) determined by the minimum gap during the adiabatic quantum optimization for an NP-hard Ising spin ...
Bang-bang control as a design principle for classical and quantum optimization algorithms
Physically motivated classical heuristic optimization algorithms such as simulated annealing (SA) treat the objective function as an energy landscape, and allow walkers to escape local minima. It has been argued that quantum properties such as tunneling ...
Enhancing quantum annealing performance for the molecular similarity problem
Quantum annealing is a promising technique which leverages quantum mechanics to solve hard optimization problems. Considerable progress has been made in the development of a physical quantum annealer, motivating the study of methods to enhance the ...
Comments