Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks

Qianke Wang; Jun Liu; Dawei Lyu; Jian Wang

doi:10.1038/s41377-023-01336-7

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Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks

Original Articles

- Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks
- Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks
- LSA 2024年13卷第1期页码：55-67
- Affiliations：
  
  1.Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074 Hubei, China
  2.Optics Valley Laboratory, Wuhan 430074 Hubei, China
- Author bio：
  
  Jian Wang (jwang@hust.edu.cn)
- Funds：
- DOI：10.1038/s41377-023-01336-7
  中图分类号：
- 收稿日期：2023-05-27，
  
  修回日期：2023-10-30，
  
  录用日期：2023-11-12，
  
  网络出版日期：2024-01-05，
  
  纸质出版日期：2024-01-31
- Accepted：
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Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks[J]. LSA, 2024,13(1):55-67.

Wang, Q. K. et al. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks.,Light: Science & Applications, 13, 55-67 (2024).
Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks[J]. LSA, 2024,13(1):55-67. DOI： 10.1038/s41377-023-01336-7.

Wang, Q. K. et al. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks.,Light: Science & Applications, 13, 55-67 (2024). DOI： 10.1038/s41377-023-01336-7.

摘要

Abstract

While the spatial mode of photons is widely used in quantum cryptography

its potential for quantum computation remains largely unexplored. Here

we showcase the use of the multi-dimensional spatial mode of photons to construct a series of high-dimensional quantum gates

achieved through the use of diffractive deep neural networks (D

NNs). Notably

our gates demonstrate high fidelity of up to 99.6(2)%

as characterized by quantum process tomography. Our experimental implementation of these gates involves a programmable array of phase layers in a compact and scalable device

capable of performing complex operations or even quantum circuits. We also demonstrate the efficacy of the D

NN gates by successfully implementing the Deutsch algorithm and propose an intelligent deployment protocol that involves self-configuration and self-optimization. Moreover

we conduct a comparative analysis of the D

NN gate's performance to the wave-front matching approach. Overall

our work opens a door for designing specific quantum gates using deep learning

with the potential for reliable execution of quantum computation.

关键词

Keywords

references

Nielsen, M. A.&Chuang, I. L. Quantum Computation and Quantum Information. (Cambridge: Cambridge University Press, 2010).

Barends, R. et al. Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature 508 , 500–503 (2014)..

Xu, Y. et al. High-fidelity, high-scalability two-qubit gate scheme for superconducting qubits. Phys. Rev. Lett. 125 , 240503 (2020)..

Ballance, C. J. et al. High-fidelity quantum logic gates using trapped-ion hyperfine qubits. Phys. Rev. Lett. 117 , 060504 (2016)..

Srinivas, R. et al. High-fidelity laser-free universal control of trapped ion qubits. Nature 597 , 209–213 (2021)..

Rong, X. et al. Experimental fault-tolerant universal quantum gates with solid-state spins under ambient conditions. Nature Communications 6 , 8748 (2015)..

Noiri, A. et al. Fast universal quantum gate above the fault-tolerance threshold in silicon. Nature 601 , 338–342 (2022)..

Mądzik, M. T. et al. Precision tomography of a three-qubit donor quantum processor in silicon. Nature 601 , 348–353 (2022)..

Xue, X. et al. Quantum logic with spin qubits crossing the surface code threshold. Nature 601 , 343–347 (2022)..

Wang, D. S., Fowler, A. G. & Hollenberg, L. C. L. Surface code quantum computing with error rates over 1%. Phys. Rev. A 83 , 020302 (2011)..

O'Brien, J. L. et al. Demonstration of an all-optical quantum controlled-NOT gate. Nature 426 , 264–267 (2003)..

Okamoto, R. et al. Demonstration of an optical quantum controlled-NOT gate without path interference. Phys. Rev. Lett. 95 , 210506 (2005)..

Babazadeh, A. et al. High-dimensional single-photon quantum gates: concepts and experiments. Phys. Rev. Lett. 119 , 180510 (2017)..

Wang, Y. L. et al. Experimental demonstration of efficient high-dimensional quantum gates with orbital angular momentum. Quantum Sci. Technol. 7 , 015016 (2022)..

Wang, F. R. et al. Experimental demonstration of a quantum controlled-SWAP gate with multiple degrees of freedom of a single photon. Quantum Sci. Technol. 6 , 035005 (2021)..

Brandt, F. et al. High-dimensional quantum gates using full-field spatial modes of photons. Optica 7 , 98–107 (2020)..

Chen, Z. & Segev, M. Highlighting photonics: looking into the next decade. eLight 1 , 2 (2021)..

Ding, Y. H. et al. High-dimensional quantum key distribution based on multicore fiber using silicon photonic integrated circuits. npj Quantum Inform. 3 , 25 (2017)..

Boaron, A. et al. Secure quantum key distribution over 421 km of optical fiber. Phys. Rev. Lett. 121 , 190502 (2018)..

Mao, Y. Q. et al. Integrating quantum key distribution with classical communications in backbone fiber network. Optics Express 26 , 6010–6020 (2018)..

Liu, J. et al. Multidimensional entanglement transport through single-mode fiber. Sci. Adv. 6 , eaay0837 (2020)..

Wang, Q. K. et a l. High-dimensional quantum cryptography with hybrid orbital-angular-momentum states through 25 km of ring-core fiber: a proof-of-concept demonstration. Phys. Rev. Appl. 15 , 064034 (2021)..

Da Lio, B. et al. Path-encoded high-dimensional quantum communication over a 2-km multicore fiber. npj Quantum Inform. 7 , 63 (2021)..

Cozzolino, D. et al. High-dimensional quantum communication: benefits, progress, and future challenges. Adv. Quantum Technol. 2 , 1900038 (2019)..

Kagalwala, K. H. et al. Single-photon three-qubit quantum logic using spatial light modulators. Nat. Commun. 8 , 739 (2017)..

Zeuner, J. et al. Integrated-optics heralded controlled-NOT gate for polarization-encoded qubits. npj Quantum Inform. 4 , 13 (2018)..

Larsen, M. V. et al. Deterministic multi-mode gates on a scalable photonic quantum computing platform. Nat. Phys. 17 , 1018–1023 (2021)..

Shi, S. et al. High-fidelity photonic quantum logic gate based on near-optimal Rydberg single-photon source. Nat. Commun. 13 , 445 4 (2022)..

Ecker, S. et al. Overcoming noise in entanglement distribution. Phys. Rev. X 9 , 041042 (2019)..

Sakamaki, Y. et al. New optical waveguide design based on wavefront matching method. J. Lightwave Technol. 25 , 3511–3518 (2007)..

Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 https://doi.org/10.48550/arXiv.1412.6980 (2017).

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl. 8 , 112 (2019)..

Li, J. X. et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photon. 1 , 046001 (2019)..

Kuschmierz, R., Scharf, E., Ortegón-Gonzále z, D. F., Glosemeyer, T. & Czarske, J. W. Ultra-thin 3D lensless fiber endoscopy using diffractive optical elements and deep neural networks. Light. Adv. Manuf. 2 , 415–424 (2021)..

Li,Y., Luo, Y., Mengu, D., Bai, B. & Ozcan, A. Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network. Light. Adv. Manuf. 4 , 1–16 (2023)..

Kulce, O. et al. All-optical synthesis of an arbitrary linear transformation using diffractive surfaces. Light Sci. Appl. 10 , 196 (2021)..

Davis, J. A. et al. Encoding amplitude information onto phase-only filters. Appl. Opt. 38 , 5004–5013 (1999)..

Bolduc, E. et al. Exact solution to simultaneous intensity and phase encryption with a single phase-only hologram. Opt. Lett. 38 , 3546–3549 (2013)..

Fontaine, N. K. et al. Laguerre-gaussian mode sorter. Nat. Commun. 10 , 1865 (2019)..

Sun, K. et al. Optical demonstration of quantum fault-tolerant threshold. Light Sci. Appl. 11 , 203 (2022)..

Fiurášek, J. & Hradil, Z. Maximum-likelihood estimation of quantum processes. Phys. Rev. A 63 , 020101 (2001)..

Ježek, M., Fiurášek, J. & Hradil, Z. Quantum inference of states and processes. Phys. Rev. A 68 , 012305 (2003)..

Deutsch, D. & Jozsa, R. Rapid solution of problems by quantum computation. Proc. R. Soc. A: Math. Phys. Eng. Sci. 439 , 553–558 (1992)..

Perez-Garcia, B. et al. Quantum computation with classical light: implementation of the Deutsch-Jozsa algorithm. Phys. Lett. A 380 , 1925–1931 (2016)..

Hashimoto, T. Wavefront matching method as a deep neural network and mutual use of their techniques. Opt. Commun. 498 , 127216 (2021)..

Zhou, T. K. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15 , 367–373 (2021)..

Mengu, D. et al. Misalignment resilient diffractive optical networks. Nanophotonics 9 , 4207–4219 (2020)..

Mengu, D., Rivenson, Y. & Ozcan, A. Scale-, shift-, and rotation-invariant diffractive optical networks. ACS Photonics 8 , 324–334 (2021)..

Zhang, Y. H. et al. An ultra-broadband polarization-insensitive optical hybrid using multiplane light conversion. J. Lightwave Technol. 38 , 6286–6291 (2020)..

Horodynski, M. et al. Anti-reflection structure for perfect transmission through complex media. Nature 607 , 281–286 (2022)..

Chang, J. L. et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Scientific Reports 8 , 12324 (2018)..

Dou, H. K. et al. Residual D 2 NN: training diffractive deep neural networks via learnable light shortcuts. Optics Lett. 45 , 2688–2691 (2020)..

Yan, T. et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett. 123 , 023901 (2019)..

Zuo, Y. et al. All-optical neural network with nonlinear activation functions. Optica 6 , 1132–1137 (2019)..

Xiong, W. J. et al. Optical diffractive deep neural network-based orbital angular momentum mode add-drop multiplexer. Optics Express 29 , 36936–36952 (2021)..

Rahman, M. S. S. et al. Ensemble learning of diffractive optical networks. Light Sci. Appl. 10 , 14 (2021)..

Gao, X. Q. et al. Arbitrary d -dimensional Pauli X gates of a flying qudit. Phys. Rev. A 99 , 023825 (2019)..

Wen, H. et al. Scalable non-mode selective Hermite-Gaussian mode multiplexer based on multi-plane light conversion. Photon. Res. 9 , 88–97 (2021)..

Rademacher, G. et al. Peta-bit-per-second optical communications system using a standard cladding diameter 15-mode fiber. Nat. Commun. 12 , 4238 (2021)..

Kulce, O. et al. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl. 10 , 25 (2021)..

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