Quantum Annealing Based Optimization of Robotic Movement in Manufacturing

  • Arpit MehtaEmail author
  • Murad Muradi
  • Selam Woldetsadick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11413)


Recently, considerable attention has been paid to planning and scheduling problems for multiple robot systems (MRS). Such attention has resulted in a wide range of techniques being developed in solving more complex tasks at ever increasing speeds. At the same time, however, the complexity of such tasks has increased as such systems have to cope with ever increasing business requirements, rendering the above mentioned techniques unreliable, if not obsolete. Quantum computing is an alternative form of computation that holds a lot of potential for providing some advantages over classical computing for solving certain kinds of difficult optimization problems in the coming years. Motivated by this fact, in this paper we demonstrate the feasibility of running a particular type of optimization problem on existing quantum computing technology. The optimization problem investigate arises when considering how to optimize a robotic assembly line, which is one of the keys to success in the manufacturing domain. A small improvement in the efficiency of such an MRS can lead to huge saving in terms of time of manufacturing, capacity, robot life, and material usage. The nature of the quantum processor used in this study imposes the constraint that the optimization problem be cast as a quadratic unconstrained binary optimization (QUBO) problem. For the specific problem we investigate, this allows situations with one robot to be modeled naturally, meanwhile modeling the multi-robot generalization is less obvious and left as a topic for future research. The results show that for simple 1-robot tasks, the optimization problem can be straightforwardly solved within a feasible time span on existing quantum computing hardware.


Quantum annealing Robotic manufacturing TSP MRS D-Wave QUBO Optimization problem 


  1. 1.
    Correl, R.: Quantum artificial intelligence and machine learning: the path to enterprise deployments. QCWare (2017).
  2. 2.
    Held, M., Karp, R.M.: The traveling-salesman problem and minimum spanning trees. Oper. Res. 18(6), 1138–1162 (1970)MathSciNetCrossRefGoogle Scholar
  3. 3.
    D.W.S, Inc.: The D-wave 2000Q quantum computer technology overview. D-Wave (2018)Google Scholar
  4. 4.
    Lucas, A.: Ising formulations of many NP problems. Front. Phys. 2, 5 (2014).
  5. 5.
    Morita, S., Nishimori, H.: Convergence theorems for quantum annealing. J. Phys. A: Math. Gen. 39(45), 13903 (2006).
  6. 6.
    Moylett, D.J., Linden, N., Montanaro, A.: Quantum speedup of the traveling-salesman problem for bounded-degree graphs. Phys. Rev. A 95, 032323 (2017). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BMW AGMunichGermany

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