Paper Info
Title
A case study in programming a quantum annealer for hard operational planning problems
Abstract
We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the performance of quantum annealers on problems native to their architecture, and others have examined performance of select problems stemming from an application area, ours is one of the first studies of a quantum annealer's performance on parametrized families of hard problems from a practical domain. We explore two different general mappings of planning problems to quadratic unconstrained binary optimization (QUBO) problems, and apply them to two parametrized families of planning problems, navigation-type and scheduling-type. We also examine two more compact, but problem-type specific, mappings to QUBO, one for the navigation-type planning problems and one for the scheduling-type planning problems. We study embedding properties and parameter setting and examine their effect on the efficiency with which the quantum annealer solves these problems. From these results, we derive insights useful for the programming and design of future quantum annealers: problem choice, the mapping used, the properties of the embedding, and the annealing profile all matter, each significantly affecting the performance.
Year
DOI
Venue
2015
10.1007/s11128-014-0892-x
Quantum Information Processing
Keywords
Field
DocType
Quantum computation,Quantum annealing keyword,Operational planning
Operational planning,Quantum,Mathematical optimization,Embedding,Parametrization,Quadratic unconstrained binary optimization,Quantum mechanics,Quantum computer,Optimization problem,Physics
Journal
Volume
Issue
ISSN
14
1
1570-0755
Citations 
PageRank 
References 
35
2.28
17
Authors
6
Name
Order
Citations
PageRank
Eleanor Rieffel148848.71
Davide Venturelli2909.06
Bryan O'gorman3565.12
Minh B. Do4352.28
Elicia M. Prystay5352.28
Vadim N. Smelyanskiy6503.27