The First Quantum Co-processor Hybrid for Processing Quantum Point Cloud Multimodal Sensor Data

  • George J. FrangouEmail author
  • Stephane Chretien
  • Ivan Rungger
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


The large-scale multimodal sensor fusion of the internet of things (IoT) data can be transformed into an N-dimensional classical point cloud. For example, the transformation may be the fusion of three imaging modalities of different natures such as LiDAR (light imaging, detection, and ranging), a set of RGB images, and a set of thermal images. However, it is not easy to process a point cloud because it can have millions or even hundreds of millions of points. Classical computers therefore often crash when operating a point cloud of multimodal sensor data. Quantum Point Clouds (QPC) address the problem of uncertainty in multi-modal sensor data, such that precognitive/predictive models can be derived with outcomes of greater certainty than classical information processing methods. This paper presents early experiments of the first application of a quantum co-processor hybrid for processing quantum point cloud multimodal sensor data from an autonomous racing car. Applied to the more complex case of cave mapping, it then describes the first hybrid classical-quantum co-processor, comprising a graphical processing unit, differential pulse code modulator and a quantum computer. The graphical processing unit comprises a multiple input/output data interface, transformation means for transforming a fused depth bitmap of the multi-modal sensor data into a point cloud representation with world coordinates, control logic that manages the multiple input/output data interface, and the differential pulse code modulator. The quantum co-processor comprises an assembly of quantum computing chips.


Quantum computing Quantum point clouds Multimodal sensor Data fusion Data uncertainty measures Autonomous systems 


  1. 1.
    Jiang, N., Hu, H., Dang, Y., Zhang, W.: Quantum point cloud and its compression. Int. J. Theor. Phys. 56, 3147–3163 (2017)CrossRefGoogle Scholar
  2. 2.
    Luo, Z., Zheng, W., Li, J., Zhao, M., Peng, X., Suter, D.: Quantum image processing and its application to edge detection: theory and experiment. Phys. Rev. X 7, 031041 (2017)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceedings of the SPIE Conference on Quantum Information and Computation, pp. 137–147 (2003)Google Scholar
  6. 6.
    Cao, M., Wang, P., Wu, L., Lu, Q., Lu, Z., Lu, Q.: The research on the online publishing platform of point clouds of chinese cultural heritage based on LIDAR technology: a case study of chen clan academy in Guangzhou, Guangdong Province. In: IOP Conference Series Materials Science and Engineering, vol. 452, p. 032019, December 2018CrossRefGoogle Scholar
  7. 7.
    Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M.: Artif. Intell. Rev. 43, 55 (2015). Scholar
  8. 8.
    Satyajit, S., Srinivasan, K., Behera, B.K., et al.: Quantum Inf. Process. 17, 212 (2018). Scholar
  9. 9.
    Gustavson, T.L., Bouyer, P., Kasevich, M.A.: Precision rotation measurements with an atom interferometer gyroscope. Phys. Rev. Lett. 78, 2046–2049 (1997)CrossRefGoogle Scholar
  10. 10.
    Chou, C.W., Hume, D.B., Rosenband, T., Wineland, D.J.: Optical clocks and relativity. Science 329, 1630–1633 (2010)CrossRefGoogle Scholar
  11. 11.
    Shah, V., Knappe, S., Schwindt, P.D.D., Kitching, J.: Subpicotesla atomic magnetometry with a microfabricated vapour cell. Nat. Photon. 1, 649–652 (2007)CrossRefGoogle Scholar
  12. 12.
    Aasi, J., et al.: Enhanced sensitivity of the LIGO gravitational wave detector by using squeezed states of light. Nat. Photon. 7, 613–619 (2013)CrossRefGoogle Scholar
  13. 13.
    Cutler, C.C.: Differential quantization of communication signals. U.S. patent 2,605,361 (filed 1950, issued 1952)Google Scholar
  14. 14.
    Wasilewski, W., et al.: Quantum noise limited and entanglement-assisted magnetometry. Phys. Rev. Lett. 104, 133601 (2010)CrossRefGoogle Scholar
  15. 15.
    Frangou, G.J.: Great Britain Provisional Patent Application 1816049: Processing Quantum Point Cloud Multimodal Sensor Data (2018)Google Scholar
  16. 16.
    Wang, J.: QRDA: quantum representation of digital audio. Int. J. Theor. Phys. 55(3), 1622–1641 (2016)CrossRefGoogle Scholar
  17. 17.
    Vlatko, V., Adriano, B., Artur, E.: Quantum networks for elementary arithmetic operations. Phys. Rev. A 54(1), 147–153 (1996)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Frangou, G.J.: U.S. Patent No. 9,645,576 Chinese Patent 1,051,892,37, Japanese Patent 2,016,520,464, Israel Patent 2,416,88, European Patent Application 2,976,240, Korean Patent Application 2,015,013,8257, PCT Patent Application 2,014,147,361: Apparatus for Controlling a Land Vehicle which is Self-Driving or Partially Self- Driving (2013)Google Scholar
  19. 19.
    Leibfried, D., et al.: Toward Heisenberg-limited spectroscopy with multiparticle entangled states. Science 304, 1476–1478 (2004)CrossRefGoogle Scholar
  20. 20.
    Zhang, Y., Lu, K., Gao, Y.H., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(12), 2833–2860 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • George J. Frangou
    • 1
    Email author
  • Stephane Chretien
    • 2
  • Ivan Rungger
    • 2
  1. 1.Massive Analytic Ltd, IDEALondonLondonUK
  2. 2.National Physical LaboratoryTeddingtonUK

Personalised recommendations