Quantum computing for optimization

From Cornell University Computational Optimization Open Textbook - Optimization Wiki
Revision as of 13:22, 23 November 2020 by Asa273 (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search


Quantum computing (QC) is the next frontier in computation and has attracted a lot of attention from the scientific community in recent years. QC provides a novel approach to help solve some of the most complex optimization problems while offering an essential speed advantage over classical methods. [1] This is evident from QC techniques like Shor’s algorithm for integer factorization, [2] and Grover's search algorithm for unstructured databases. [3] Quantum adiabatic algorithms too are efficient optimization strategies that quickly search over the solution space. Quantum computers perform computation by inducing quantum speedups whose scaling far exceeds the capability of the most powerful classical computers. QC’s major applications can be perceived in areas of optimization, machine learning, cryptography, and quantum chemistry. [4] Despite the contrasting views on QC’s viability and performance, there is no doubt that QC holds great promise to open up a new era of computing.


  1. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information Cambridge University Press, 2010, p. 702.
  2. P. W. Shor, "Algorithms for quantum computation: discrete logarithms and factoring," presented at the Proceedings 35th Annual Symposium on Foundations of Computer Science, 1994.
  3. L. K. Grover, "Quantum Mechanics Helps in Searching for a Needle in a Haystack," Physical Review Letters, vol. 79, pp. 325-328, 1997.
  4. J. Preskill, "Quantum Computing in the NISQ era and beyond"