Skip to main content
Log in

Circuit implementation of discrete-time quantum walks via the shunt decomposition method

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

Several models have been proposed to build evolution operators to perform quantum walks in a theoretical way, although when wanting to map the resulting evolution operators into quantum circuits to run them in quantum computers, it is often the case that the mapping process is in fact complicated. Nevertheless, when the adjacency matrix of a graph can be decomposed into a sum of permutation matrices, we can always build a shift operator for a quantum walk that has a block diagonal matrix representation. In this paper, we analyze the mapping process of block diagonal operators into quantum circuit form and apply this method to obtain quantum circuits that generate quantum walks on the most common topologies found in the literature: the straight line, the cyclic graph, the hypercube and the complete graph. The obtained circuits are then executed on quantum processors of the type Falcon r5.11 L and Falcon r4T (two of each type) through IBM Quantum Composer platform and on the Qiskit Aer simulator, performing three steps for each topology. The resulting distributions were compared against analytical distributions, using the statistical distance \(\ell _1\) as a performance metric. Regarding experimental executions, we obtained short \(\ell _1\) distances in the cases of quantum circuits with a low amount of multi-control gates, being the quantum processors of the type Falcon r4T the ones that provided more accurate results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Notes

  1. Two’s complement is a way to represent both positive and negative numbers in bitstring notation. Strings whose leftmost bit \(b_n = 0\), correspond to positive numbers. When \(b_n = 1\), we have a negative number. The rest of the bits, \(N=b_{n-1}\dots b_{2}b_{1}\), represent the value of the number and \(b_{n}\) only indicates the symbol. When \(b_{n} = 0\), the right substring, N, has the same relation with decimal numbers as in binary notation. However, when \(b_{n} = 1\), the relation of N with decimal numbers is inverted with respect to binary notation. For example, for a 3-bit string, let \(b_n = 1\), then for N equals 00, 01, 10 and 11 the associated numbers are 4, 3, 2 and 1, respectively, in such a way that 100, 101, 110 and 111 are associated with \(-4\), \(-3\), \(-2\) and \(-1\), respectively.

References

  1. Lawler, G.F., Limic, V.: Random walk: A modern introduction (2010). https://doi.org/10.1017/CBO9780511750854

    Article  Google Scholar 

  2. Weiss, G.H.: Random walks and their applications: Widely used as mathematical models, random walks play an important role in several areas of physics, chemistry, and biology. Am. Sci. 71(1), 65–71 (1983)

    ADS  Google Scholar 

  3. Codling, E.A., Plank, M.J., Benhamou, S.: Random walk models in biology. J. R. Soc. Interface 5(25), 813–834 (2008). https://doi.org/10.1098/rsif.2008.0014

    Article  Google Scholar 

  4. Lee, C., Jang, W.-D., Sim, J.-Y., Kim, C.-S.: Multiple random walkers and their application to image cosegmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3837–3845 (2015). https://doi.org/10.1109/CVPR.2015.7299008

  5. Abakah, E.J., Alagidede, P., Mensah, L., Ohene-Asare, K.: Non-linear approach to random walk test in selected African countries. Int. J. Manag. Finance 14(3), 362–376 (2018). https://doi.org/10.1108/ijmf-10-2017-0235

    Article  Google Scholar 

  6. Kendon, V.: Quantum walk computation. AIP Conference Proceedings 1633(1), 177–179 (2014) https://aip.scitation.org/doi/pdf/10.1063/1.4903129. https://doi.org/10.1063/1.4903129

  7. Krovi, H., Magniez, F., Ozols, M., Roland, J.: Quantum walks can find a marked element on any graph. Algorithmica 74(2), 851–907 (2015). https://doi.org/10.1007/s00453-015-9979-8

    Article  MathSciNet  MATH  Google Scholar 

  8. Aharonov, D., Ambainis, A., Kempe, J., Vazirani, U.: Quantum Walks On Graphs (2002)

  9. Farhi, E., Gutmann, S.: Quantum computation and decision trees. Phys. Rev. A 58(2), 915–928 (1998). https://doi.org/10.1103/physreva.58.915

    Article  ADS  MathSciNet  Google Scholar 

  10. Chandrashekar, C.M.: Discrete-Time Quantum Walk–Dynamics and Applications (2010)

  11. Szegedy, M.: Quantum speed-up of markov chain based algorithms. In: 45th Annual IEEE Symposium on Foundations of Computer Science (2004). https://doi.org/10.1109/focs.2004.53

  12. Portugal, R.: Staggered quantum walks on graphs. Phys. Rev. A 93, 062335 (2016). https://doi.org/10.1103/PhysRevA.93.062335

    Article  ADS  MathSciNet  Google Scholar 

  13. Venegas-Andraca, S.: Quantum walk: a comprehensive review. Quantum Inf. Process. 11(5), 1015–1106 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Yang, Y.-G., Pan, Q.-X., Sun, S.-J., Xu, P.: Novel image encryption based on quantum walks. Sci. Rep. 5(1), (2015). https://doi.org/10.1038/srep07784

  15. Vlachou, C., Rodrigues, J., Mateus, P., Paunković, N., Souto, A.: Quantum walk public-key cryptographic system. Int. J. Quant. Inf. 13(07), 1550050 (2015). https://doi.org/10.1142/s0219749915500501

    Article  MathSciNet  MATH  Google Scholar 

  16. Shenvi, N., Kempe, J., Whaley, K.B.: Quantum random-walk search algorithm. Phys. Rev. A 67(5) (2003). https://doi.org/10.1103/physreva.67.052307

  17. Bezerra, G.A., Lugão, P.H., Portugal, R.: Quantum-walk-based search algorithms with multiple marked vertices. Phys. Rev. A 103(6) (2021). https://doi.org/10.1103/physreva.103.062202

  18. Dernbach, S., Mohseni-Kabir, A., Pal, S., Gepner, M., Towsley, D.: Quantum walk neural networks with feature dependent coins. Appl. Netw. Sci. 4(1) (2019). https://doi.org/10.1007/s41109-019-0188-2

  19. de Souza, L.S., de Carvalho, J.H.A., Ferreira, T.A.E.: Quantum walk to train a classical artificial neural network. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pp. 836–841 (2019). https://doi.org/10.1109/BRACIS.2019.00149

  20. Paparo, G.D., Martin-Delgado, M.A.: Google in a quantum network. Sci. Rep. 2(1) (2012). https://doi.org/10.1038/srep00444

  21. Chawla, P., Mangal, R., Chandrashekar, C.M.: Discrete-time quantum walk algorithm for ranking nodes on a network. Quantum Inf. Process. 19(5) (2020). https://doi.org/10.1007/s11128-020-02650-4

  22. Tulsi, A.: Faster quantum-walk algorithm for the two-dimensional spatial search. Phys. Rev. A 78, 012310 (2008). https://doi.org/10.1103/PhysRevA.78.012310

    Article  ADS  MATH  Google Scholar 

  23. Preskill, J.: Quantum computing in the nisq era and beyond. Quantum 2, 79 (2018). https://doi.org/10.22331/q-2018-08-06-79

  24. Schreiber, A., Cassemiro, K.N., Poto ček, V., Gábris, A., Mosley, P.J., Andersson, E., Jex, I., Silberhorn, C.: Photons walking the line: A quantum walk with adjustable coin operations. Phys. Rev. Lett. 104, 050502 (2010). https://doi.org/10.1103/PhysRevLett.104.050502

  25. Broome, M.A., Fedrizzi, A., Lanyon, B.P., Kassal, I., Aspuru-Guzik, A., White, A.G.: Discrete single-photon quantum walks with tunable decoherence. Phys. Rev. Lett. 104, 153602 (2010). https://doi.org/10.1103/PhysRevLett.104.153602

    Article  ADS  Google Scholar 

  26. Shakeel, A.: Efficient and scalable quantum walk algorithms via the quantum fourier transform. Quantum Information Processing 19(9) (2020). https://doi.org/10.1007/s11128-020-02834-y

  27. Georgopoulos, K., Emary, C., Zuliani, P.: Comparison of quantum-walk implementations on noisy intermediate-scale quantum computers. Phys. Rev. A 103, 022408 (2021). https://doi.org/10.1103/PhysRevA.103.022408

    Article  ADS  MathSciNet  Google Scholar 

  28. Acasiete, F., Agostini, F.P., Moqadam, J.K., Portugal, R.: Implementation of quantum walks on ibm quantum computers. Quantum Inf. Process. 19(12) (2020). https://doi.org/10.1007/s11128-020-02938-5

  29. Balu, R., Castillo, D., Siopsis, G.: Physical realization of topological quantum walks on IBM-q and beyond. Quant. Sci. Technol 3(3), 035001 (2018). https://doi.org/10.1088/2058-9565/aab823

    Article  ADS  Google Scholar 

  30. Tang, H., Lin, X.-F., Feng, Z., Chen, J.-Y., Gao, J., Sun, K., Wang, C.-Y., Lai, P.-C., Xu, X.-Y., Wang, Y., et al.: Experimental two-dimensional quantum walk on a photonic chip. Sci. Adv. 4(5) (2018). https://doi.org/10.1126/sciadv.aat3174

  31. Qiang, X., Loke, T., Montanaro, A., Aungskunsiri, K., Zhou, X., O’Brien, J.L., Wang, J.B., Matthews, J.C.: Efficient quantum walk on a quantum processor. Nat. Commun. 7(1) (2016). https://doi.org/10.1038/ncomms11511

  32. Jiao, Z.-Q., Gao, J., Zhou, W.-H., Wang, X.-W., Ren, R.-J., Xu, X.-Y., Qiao, L.-F., Wang, Y., Jin, X.-M.: Two-dimensional quantum walks of correlated photons. Optica 8(9), 1129–1135 (2021). https://doi.org/10.1364/OPTICA.425879

    Article  ADS  Google Scholar 

  33. Godsil, C., Zhan, H.: Discrete-time quantum walks and graph structures. J. Combinat. Theory Ser. A 167, 181–212 (2019). https://doi.org/10.1016/j.jcta.2019.05.003

    Article  MathSciNet  MATH  Google Scholar 

  34. Montanaro, A.: Quantum walks on directed graphs. Quantum Inf. Comput. 7(1 &2), 93–102 (2007). https://doi.org/10.26421/qic7.1-2-5

  35. Carnia, E., Suyudi, M., Aisah, I., Supriatna, A.K.: A review on eigen values of adjacency matrix of graph with cliques. AIP Conf. Proc. (2017). https://doi.org/10.1063/1.4995116

    Article  Google Scholar 

  36. Li, X., Yang, G., Torres, C.L., Zheng, D., Wang, K.L.: A class of efficient quantum incrementer gates for quantum circuit synthesis. Int. J. Mod. Phys. B 28(01), 1350191 (2013). https://doi.org/10.1142/s0217979213501919

    Article  ADS  MathSciNet  MATH  Google Scholar 

  37. Golub, G. Van Loan, C.: Matrix computations, 4th Edition. Johns Hopkins Studies in the Mathematical Sciences, Johns Hopkins University Press, Baltimore (2013)

  38. Mottonen, M., Vartiainen, J.J.: Decompositions of general quantum gates. (2005) arXiv:quant-ph/0504100

  39. Tucci, R.R.: QC Paulinesia. (2004). https://doi.org/10.1007/978-1-4. arXiv:abs/quant-ph/0407215

  40. LI, C.-K., Roberts, R., Yin, X.: Decomposition of unitary matrices and quantum gates. Int. J. Quantum Inf. 11(01), 1350015 (2013). https://doi.org/10.1142/s0219749913500159

  41. Rotman, J.J.: An introduction to the theory of groups, 4th Edition. Springer, New York, NY, (1994). https://doi.org/10.1007/978-1-4612-4176-8

  42. Olver, F.W.J., et al.: NIST Digital Library of Mathematical Functions (2020)

  43. Florkowski, S.F.: Spectral graph theory of the hypercube. PhD thesis, Naval Postgraduate School (2008)

  44. Douglas, B.L., Wang, J.B.: Efficient quantum circuit implementation of quantum walks. Phys. Rev. A 79(5) (2009). https://doi.org/10.1103/physreva.79.052335

  45. Daraeizadeh, S., Kumar, P.: Efficient implementation of multi-control toffoli gates in linear nearest neighbor arrays. PhD thesis, Wichita State University (2014)

  46. Rahman, M.Z., Rice, J.E.: Templates for positive and negative control toffoli networks. Reversible Comput. 125–136 (2014). https://doi.org/10.1007/978-3-319-08494-7_10

  47. Cheng, X., Guan, Z., Wang, W., Zhu, L.: A simplification algorithm for reversible logic network of positive/negative control gates. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (2012). https://doi.org/10.1109/fskd.2012.6233837

  48. Arabzadeh, M., Saeedi, M., Zamani, M.S.: Rule-based optimization of reversible circuits. In: 2010 15th Asia and South Pacific Design Automation Conference (ASP-DAC) (2010). https://doi.org/10.1109/aspdac.2010.5419685

  49. IBM quantum. https://quantum-computing.ibm.com/

  50. IBM quantum processor types. https://quantum-computing.ibm.com/composer/docs/iqx/manage/systems/processors

  51. Wing, A.: Allanwing-QC/quantum-walks-via-shunt-decomposition-circuits (2022). https://github.com/allanwing-qc/Quantum-Walks-via-Shunt-Decomposition-Circuits

  52. Mandviwalla, A., Ohshiro, K., Ji, B.: Implementing Grover’s algorithm on the IBM quantum computers. In: 2018 IEEE International Conference on Big Data (Big Data), 2531–2537 (2018). https://doi.org/10.1109/BigData.2018.8622457

  53. Liu, J., Bello, L., Zhou, H.: Relaxed peephole optimization: A novel compiler optimization for quantum circuits (2020). arXiv:2012.07711

  54. Heese, R., Bickert, P., Niederle, A.E.: Representation of binary classification trees with binary features by quantum circuits. Quantum 6, 676 (2022). https://doi.org/10.22331/q-2022-03-30-676

  55. Moore, C., Russell, A.: Quantum Walks on the Hypercube (2001)

  56. Portugal, R.: Quantum walks and search algorithms (2019). https://doi.org/10.1007/978-1-4614-6336-8

    Article  Google Scholar 

  57. Makmal, A., Zhu, M., Manzano, D., Tiersch, M., Briegel, H.J.: Quantum walks on embedded hypercubes. Phys. Rev. A 90(2) (2014). https://doi.org/10.1103/physreva.90.022314

Download references

Acknowledgements

Both authors acknowledge the financial support provided by Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias and Consejo Nacional de Ciencia y Tecnología (CONACyT). SEVA acknowledges the support of CONACyT-SNI [SNI number 41594].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salvador E. Venegas-Andraca.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wing-Bocanegra, A., Venegas-Andraca, S.E. Circuit implementation of discrete-time quantum walks via the shunt decomposition method. Quantum Inf Process 22, 146 (2023). https://doi.org/10.1007/s11128-023-03878-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-023-03878-6

Keywords

Navigation