A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Huihui Zhu
  • Haosen Chen
  • Shuyi Li
  • Tian Chen
  • Yuan Li
  • Xianshu Luo
  • Feng Gao
  • Qiang Li
  • Linjie Zhou
  • Muhammad Faeyz Karim
  • Xiaopeng Shang
  • Fei Duan
  • Hong Cai
  • Leong Chuan Kwek
  • Xiangdong Zhang
  • Ai-Qun Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number2300304
Journal / PublicationLaser and Photonics Reviews
Volume18
Issue number2
Online published27 Nov 2023
Publication statusPublished - Feb 2024

Abstract

Quantum computing has grown extensively, especially in system design and development, and the current research focus has gradually evolved from validating quantum advantage to practical applications. In particular, nondeterministic-polynomial-time (NP)-complete problems are central in numerous important application areas. Still, in practice, it is difficult to solved efficiently with conventional computers, limited by the exponential jump in hardness. Here, a quantum photonic microprocessor based on Gaussian boson sampling (GBS) that offers dynamic programmability to solve various graph-related NP-complete problems is demonstrated. The system with optical, electrical, and thermal packaging implements a GBS with 16 modes of single-mode squeezed vacuum states, a universal programmable 16-mode interferometer, and a single photon readout on all outputs with high accuracy, generality, and controllability. The developed system is applied to demonstrate applications in solving NP-complete problems, manifesting the ability of photonic quantum computing to realize practical applications for conventionally intractable computations. The GBS-based quantum photonic microprocessor is applied to solve task assignment, Boolean satisfiability, graph clique, max cut, and vertex cover. These demonstrations suggest an excellent benchmarking platform, paving the way toward large-scale combinatorial optimization. © 2023 Wiley-VCH GmbH.

Research Area(s)

  • graph-related NP-complete problems, integrated photonics, optical quantum computing

Citation Format(s)

A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation. / Zhu, Huihui; Chen, Haosen; Li, Shuyi et al.
In: Laser and Photonics Reviews, Vol. 18, No. 2, 2300304, 02.2024.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review