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Implementing gate operations between uncoupled qubits in linear nearest neighbor arrays using a learning algorithm

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Abstract

We propose a new scheme to implement gate operations in a one dimensional linear nearest neighbor array, by using dynamic learning algorithm. This is accomplished by training quantum system using a back propagation technique, to find the system parameters that implement gate operations directly. The key feature of our scheme is that, we can reduce the computational overhead of a quantum circuit by finding the parameters to implement the desired gate operation directly, without decomposing them into a sequence of elementary gate operations. We show how the training algorithm can be used as a tool for finding the parameters for implementing controlled-NOT (CNOT) and Toffoli gates between next-to-nearest neighbor qubits in an Ising-coupled linear nearest neighbor system. We then show how the scheme can be used to find parameters for realizing swap gates first, between two adjacent qubits and then, between two next-to-nearest-neighbor qubits, in each case without decomposing it into 3 CNOT gates. Finally, we show how the scheme can be extended to systems with non-diagonal interactions. To demonstrate, we train a quantum system with Heisenberg interactions to find the parameters to realize a swap operation.

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Abbreviations

LNN:

Linear nearest neighbor

CNOT:

Controlled-NOT

RMS:

Root mean square

References

  1. Kane, B.E.: A silicon-based nuclear spin quantum computer. Nat. Lond. 393, 133–136 (1998)

    Article  ADS  Google Scholar 

  2. Hollenberg, L.C.L., Dzurak, A.S., Wellard, C., Hamilton, A.R., Reilly, D.J., Milburn, G.J., Clark, R.G.: Charge-based quantum computing using single donors in semiconductors. Phys. Rev. B 69, 113301–113304 (2004)

    Article  ADS  Google Scholar 

  3. Pachos, J.K., Knight, P.L.: Quantum computation with a one-dimensional optical lattice. Phys. Rev. Lett. 91, 107902–107905 (2003)

    Article  ADS  Google Scholar 

  4. Friesen, M., Rugheimer, P., Savage, D.E., Lagally, M.G., van der Weide, D.W., Joynt, R., Eriksson, M.A.: Practical design and simulation of silicon-based quantum-dot qubits. Phys. Rev. B 67, 121301–121304 (R) (2003)

    Google Scholar 

  5. Ladd, T.D., Goldman, J.R., Yamaguchi, F., Yamamoto, Y.: All-silicon quantum computer. Phys. Rev. Lett. 89, 017901–017904 (2002)

    Article  ADS  Google Scholar 

  6. Novais, E., Castro Neto, A.H.: Nuclear spin qubits in a pseudospin quantum chain. Phys. Rev. A 69, 062312–062317 (2004)

    Article  ADS  Google Scholar 

  7. Tian, L., Zoller, P.: Quantum computing with atomic Josephson-junction arrays. Phys. Rev. A 68, 042321–042330 (2003)

    Article  ADS  Google Scholar 

  8. van der Ploeg, S.H.W., Izmalkov, A., van den Brink, A.M., Hübner, U., Grajcar, M., Il’ichev, E., Meyer, H.-G., Zagoskin, A.M.: Controllable coupling of superconducting flux qubits. Phys. Rev. Lett. 98, 057004–057007 (2007)

    Article  ADS  Google Scholar 

  9. Lantz, J., Wallquist, M., Shumeiko, V.S., Wendin, G.: Josephson junction qubit network with current-controlled interaction. Phys. Rev. B 70, 140507–140510 (R) (2004)

    Google Scholar 

  10. Stock, R., James, D.F.V.: Scalable, high-speed measurement-based quantum computer using trapped ions. Phys. Rev. Lett. 102, 170501–170504 (2009)

    Article  ADS  Google Scholar 

  11. Van Meter, R., Ladd, T.D., Fowler, A.G., Yamamoto, Y.: Distributed quantum computation architecture using semiconductor nanophotonics. Int. J. Quantum Inf. 8, 295–323 (2010)

    Article  Google Scholar 

  12. Shende, V.V., Bullock, S.S., Markov, I.L.: Synthesis of quantum-logic circuits. IEEE Trans. CAD 25(6), 1000–1010 (2006)

    Google Scholar 

  13. Cheung, D., Maslov, D., Severini, S.: Translation techniques between quantum circuit architectures. In: Workshop on, Quantum Information Processing (December 2007)

  14. Chakrabarti, A., Sur-Kolay, S.: Nearest neighbor based synthesis of quantum Boolean circuits. Eng. Lett. 15, 356–361 (2007)

    Google Scholar 

  15. Khan, M.H.A.: Cost reduction in nearest neighbor based synthesis of quantum Boolean circuits. Eng. Lett. 16, 1–5 (2008)

    ADS  Google Scholar 

  16. Hirata, Y., Nakanishi, M., Yamashita, S., Nakashima, Y.: An efficient method to convert arbitrary quantum circuits to ones on a linear nearest neighbor architecture, pp. 26–33. In: International Conference on Quantum, Nano and Micro Technologies (2009)

  17. Chakrabarti, A., Sur-Kolay, S.: Rules for synthesizing quantum Boolean circuits using minimized nearest-neighbor templates, pp. 183–189. In: International Conference on Advanced Computing and, Communications (2007)

  18. Saeedi, M., Wille, R., Drechsler, R.: Synthesis of quantum circuits for linear nearest neighbor architectures. Quantum Inf. Process. 10, 355–377 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Kumar, P.: Efficient quantum computing between remote qubits in linear nearest neighbor architectures. Quantum Inf. Process. doi:10.1007/s11128-012-0485-5

  20. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge, England (2001)

    Google Scholar 

  21. Behrman, E.C., Steck, J.E., Kumar, P., Walsh, K.A.: Quantum algorithm design using dynamic learning. Quantum Inf. Comput. 8(1 &2), 0012–0029 (2008)

    MathSciNet  Google Scholar 

  22. Jozsa, R.: Fidelity for mixed quantum states. J. Mod. Opt. 41(12), 2315–2323 (1994)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  23. Khaneja, N., Reiss, T., Kehlet, C., Schulte-Herbruggen, T., Glaser, S.J.: Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms. J. Magn. Reson. 172, 296–305 (2005)

    Article  ADS  Google Scholar 

  24. Rowland, B., Jones, J.A.: Implementing quantum logic gates with gradient ascent pulse engineering: principles and practicalities. Phil. Trans. R. Soc. A 370, 4636–4650 (2012)

    Article  MathSciNet  ADS  Google Scholar 

  25. Tsai, D.B., Goan, H.: Gradient ascent pulse engineering approach to CNOT gates in donor electron spin quantum computing. AIP Conf. Proc. Solid-State Quantum Comput. 1074, 50–54 (2008)

    ADS  Google Scholar 

  26. Schuch, N., Siewert, J.: Natural two-qubit gate for quantum computation using the XY interaction. Phys. Rev. A 67, 032301–032308 (2003)

    Article  MathSciNet  ADS  Google Scholar 

  27. Makhlin, Y., Schon, G., Shnirman, A.: Quantum state engineering with Josephson-junction devices. Rev. Mod. Phys. 73, 357–400 (2001)

    Article  ADS  Google Scholar 

  28. Kumar, P., Skinner, S.R., Behrman, E.C., Steck, J.E., Zhou, Z., Han, S.: Quantum gates using a pulsed bias scheme. Phys. Rev. A. 72, 042311–042319 (2005)

    Article  ADS  Google Scholar 

  29. Kumar, P., Skinner, S.R., Daraeizadeh, S.: A nearest neighbor architecture to overcome dephasing. Quantum Inf. Process. 12, 157–188 (2013)

    Google Scholar 

  30. Loss, D., DiVincenzo, D.P.: Quantum computation with quantum dots. Phys. Rev. A 57, 120–126 (1998)

    Article  ADS  Google Scholar 

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Acknowledgments

This material is based upon work supported, in part, by the National Science Foundation under Award No. EPS-0903806 and matching support from the State of Kansas through Kansas Technology Enterprise Corporation. We would like to thank Dr. James Steck and Dr. Elizabeth Behrman for providing some of the source code to run simulations.

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Correspondence to Preethika Kumar.

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Garigipati, R.C., Kumar, P. Implementing gate operations between uncoupled qubits in linear nearest neighbor arrays using a learning algorithm. Quantum Inf Process 12, 2291–2308 (2013). https://doi.org/10.1007/s11128-013-0526-8

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  • DOI: https://doi.org/10.1007/s11128-013-0526-8

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