Automating quantum experiment control

From circuit compilation to ion routing
  • Kelly E. Stevens
  • Jason M. Amini
  • S. Charles Doret
  • Greg Mohler
  • Curtis Volin
  • Alexa W. Harter
Article
Part of the following topical collections:
  1. Trapped Ion Quantum Information Processing

Abstract

The field of quantum information processing is rapidly advancing. As the control of quantum systems approaches the level needed for useful computation, the physical hardware underlying the quantum systems is becoming increasingly complex. It is already becoming impractical to manually code control for the larger hardware implementations. In this chapter, we will employ an approach to the problem of system control that parallels compiler design for a classical computer. We will start with a candidate quantum computing technology, the surface electrode ion trap, and build a system instruction language which can be generated from a simple machine-independent programming language via compilation. We incorporate compile time generation of ion routing that separates the algorithm description from the physical geometry of the hardware. Extending this approach to automatic routing at run time allows for automated initialization of qubit number and placement and additionally allows for automated recovery after catastrophic events such as qubit loss. To show that these systems can handle real hardware, we present a simple demonstration system that routes two ions around a multi-zone ion trap and handles ion loss and ion placement. While we will mainly use examples from transport-based ion trap quantum computing, many of the issues and solutions are applicable to other architectures.

Keywords

Quantum computation Ion trap Circuit compilation Hardware control Scaling 

References

  1. 1.
    Rowe, M.A., Ben-Kish, A., DeMarco, B., Leibfried, D., Meyer, V., Beall, J., Britton, J., Hughes, J., Itano, W.M., Jelenkovic, B., et al.: Transport of quantum states and separation of ions in a dual RF ion trap. Quantum Inf. Comput. 2, 257–271 (2002)MATHGoogle Scholar
  2. 2.
    Hensinger, W.K., Olmschenk, S., Stick, D., Hucul, D., Yeo, M., Acton, M., Deslauriers, L., Monroe, C., Rabchuk, J.: T-junction ion trap array for two-dimensional ion shuttling, storage, and manipulation. Appl. Phys. Lett. 88, 034101 (2006)ADSCrossRefGoogle Scholar
  3. 3.
    Schulz, S.A., Poschinger, U.G., Singer, K., Schmidt-Kaler, F.: Optimization of segmented linear Paul traps and transport of stored particles. Prog. Phys. 54, 648 (2006)Google Scholar
  4. 4.
    Blakestad, R., Ospelkaus, C., VanDevender, A., Amini, J., Britton, J., Leibfried, D., Wineland, D.: High-fidelity transport of trapped-ion qubits through an X-junction trap array. Phys. Rev. Lett. 102, 153002 (2009)ADSCrossRefGoogle Scholar
  5. 5.
    Chiaverini, J., Blakestad, R.B., Britton, J., Jost, J.D., Langer, C., Leibfried, D., Ozeri, R., Wineland, D.J.: Surface-electrode architecture for ion-trap quantum information processing. Quantum Inf. Comput. 5, 419–439 (2005)MathSciNetMATHGoogle Scholar
  6. 6.
    Seidelin, S., Chiaverini, J., Reichle, R., Bollinger, J., Leibfried, D., Britton, J., Wesenberg, J., Blakestad, R., Epstein, R., Hume, D., et al.: Microfabricated surface-electrode ion trap for scalable quantum information processing. Phys. Rev. Lett. 96, 253003 (2006)ADSCrossRefGoogle Scholar
  7. 7.
    Moehring, D.L., Highstrete, C., Stick, D., Fortier, K.M., Haltli, R., Tigges, C., Blain, M.G.: Design, fabrication and experimental demonstration of junction surface ion traps. New J. Phys. 13, 075018 (2011)ADSCrossRefGoogle Scholar
  8. 8.
    Hughes, M.D., Lekitsch, B., Broersma, J.A., Hensinger, W.K.: Microfabricated ion traps. Contemp. Phys. 52, 505–529 (2011)ADSCrossRefGoogle Scholar
  9. 9.
    Wright, K., Amini, J.M., Faircloth, D.L., Volin, C., Doret, S.C., Hayden, H., Pai, C.S., Landgren, D.W., Denison, D., Killian, T., Slusher, R.E., Harter, A.W.: Reliable transport through a microfabricated X-junction surface-electrode ion trap. New J. Phys. 15, 033004 (2013)ADSCrossRefGoogle Scholar
  10. 10.
    Kielpinski, D., Monroe, C., Wineland, D.J.: Architecture for a large-scale ion-trap quantum computer. Nature 417, 709–711 (2002)ADSCrossRefGoogle Scholar
  11. 11.
    Amini, J.M., Uys, H., Wesenberg, J., Seidelin, S., Britton, J., Bollinger, J., Leibfried, D., Ospelkaus, C., VanDevender, A., Wineland, D.: Toward scalable ion traps for quantum information processing. New J. Phys. 12, 033031 (2010)ADSCrossRefGoogle Scholar
  12. 12.
    Jost, J.D., Home, J.P., Amini, J.M., Hanneke, D., Ozeri, R., Langer, C., Bollinger, J.J., Leibfried, D., Wineland, D.J.: Entangled mechanical oscillators. Nature 459, 683 (2009)ADSCrossRefGoogle Scholar
  13. 13.
    Aaronson, S., Gottesman, D.: Improved simulation of stabilizer circuits. Phys. Rev. A 70, 052328 (2004)ADSCrossRefGoogle Scholar
  14. 14.
    Green, A.S., Lumsdaine, P.L., Ross, N.J., Selinger, P., Valiron, B.: Quipper: a scalable quantum programming language. CoRR arXiv:1304.3390 (2013)
  15. 15.
    Oemer, B.: Classical concepts in quantum programming. Int. J. Theor. Phys. 44, 943–955 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Selinger, P.: Towards a quantum programming language. Math. Struct. Comput. Sci. 14, 527–586 (2004)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Bettelli, S., Serafini, L., Calarco, T.: Toward an architecture for quantum programming. CoRR arXiv:cs.PL/0103009 (2001)
  18. 18.
    Zuliani, P.: Logical reversibility. IBM J. Res. Dev. 45, 807–818 (2001)CrossRefGoogle Scholar
  19. 19.
    Tomita, Y., Gutiérrez, M., Kabytayev, C., Brown, K.R., Hutsel, M.R., Morris, A.P., Stevens, K.E., Mohler, G.: Comparison of ancilla preparation and measurement procedures for the Steane [[7,1,3]] code on a model ion-trap quantum computer. Phys. Rev. A 88, 042336 (2013)ADSCrossRefGoogle Scholar
  20. 20.
    Doret, S.C., Amini, J.M., Wright, K., Volin, C., Killian, T., Ozakin, A., Denison, D., Hayden, H., Pai, C., Slusher, R.E., et al.: Controlling trapping potentials and stray electric fields in a microfabricated ion trap through design and compensation. New J. Phys. 14, 073012 (2012)ADSCrossRefGoogle Scholar
  21. 21.
    Splatt, F., Harlander, M., Brownnutt, M., Zhringer, F., Blatt, R., Hänsel, W.: Deterministic reordering of 40 Ca + ions in a linear segmented Paul trap. New J. Phys. 11, 103008 (2009)ADSCrossRefGoogle Scholar
  22. 22.
    Monz, T., Nigg, D., Martinez, E.A., Brandl, M.F., Schindler, P., Rines, R., Wang, S.X., Chuang, I.L., Blatt, R.: Realization of a scalable Shor algorithm. Science 351, 1068 (2016)ADSMathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Debnath, S., Linke, N.M., Figgatt, C., Landsman, K.A., Wright, K., Monroe, C.: Demonstration of a small programmable quantum computer with atomic qubits. Nature 536, 63 (2016)Google Scholar
  24. 24.
    Hucul, D., Inlek, I.V., Vittorini, G., Crocker, C., Debnath, S., Clark, S.M., Monroe, C.: Modular entanglement of atomic qubits using photons and phonons. Nat. Phys. 11, 37 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Quantum Systems Group, Advanced Concepts LaboratoryGeorgia Tech Research InstituteAtlantaUSA
  2. 2.Department of PhysicsWilliams CollegeWilliamstownUSA

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