How Quantum Computing Can Help With (Continuous) Optimization

Authors

Christian Ayub, The University of Texas at El PasoFollow
Martine Ceberio, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

3-2019

Comments

Technical Report: UTEP-CS-19-27

To appear in Proceedings of the 12th International Workshop on Constraint Programming and Decision Making CoProd'2019, Part of the World Congress of the International Fuzzy Systems Association and the Annual Conference of the North American Fuzzy Information Processing Society IFSA/NAFIPS'2019, Lafayette, Louisiana, June 17, 2019.

Abstract

It is known that the use of quantum computing can reduced the time needed for a search in an unsorted array: from the original non-quantum time T to a much smaller quantum computation time T_q proportional to the square root √(T) of T. In this paper, we show that for a continuous optimization problem, with quantum computing, we can reach almost the same speed-up: namely, we can reduce the non-quantum time T to a much shorter quantum computation time √(T) * ln(T).