Solving Graph Problems Using Gaussian Boson Sampling

Deng, Yuping; Gong, Si-Qiu; Yi-Chao, Gu,; Zhi-Jiong, Zhang,; Hua-Liang, Liu,; Sheng, Hao; Tang, Haoyang; Xu, Jia-Min; Jia, Meng-Hao; Chen, Ming-Cheng; Zhong, Han-Sen; Wang, Hui; Yan, Jiarong; Hu, Yi; Huang, Jen Hung; Zhang, Weijun; Li, Hao; Xiao, Jing; You, Lixing; Wang, Zhen; Li, Li; Liu, Nai-Le; Lu, Chao‐Yang; Pan, Jian-Wei

doi:10.1103/physrevlett.130.190601

Cited by 16 publications

(3 citation statements)

References 38 publications

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“…Both the total number of optical modes and detected photons in GBS experiments have surpassed several hundred 7 , 8 . Moreover, it is experimentally verified that GBS devices can enhance the classical stochastic algorithms in searching some graph features 10 , 11 .…”

Section: Introductionmentioning

confidence: 91%

“…Gaussian boson sampling (GBS) is a variant of boson sampling (BS) that was originally proposed to demonstrate the quantum advantage 1 – 4 . In recent years, great progress has been made in experiments on GBS 5 – 11 . Both the total number of optical modes and detected photons in GBS experiments have surpassed several hundred 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

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Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Yang,

Wang

2024

Sci Rep

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Gaussian boson sampling (GBS) plays a crucially important role in demonstrating quantum advantage. As a major imperfection, the limited connectivity of the linear optical network weakens the quantum advantage result in recent experiments. In this work, we introduce an enhanced classical algorithm for simulating GBS processes with limited connectivity. It computes the loop Hafnian of an $$n \times n$$ n × n symmetric matrix with bandwidth w in $$O(nw2^w)$$ O ( n w 2 w ) time. It is better than the previous fastest algorithm which runs in $$O(nw^2 2^w)$$ O ( n w 2 2 w ) time. This classical algorithm is helpful on clarifying how limited connectivity affects the computational complexity of GBS and tightening the boundary for achieving quantum advantage in the GBS problem.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Yang,

Wang

2024

Sci Rep

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“…The design of the optical network used in Boson sampling produces different output probability distributions, which can encode solutions to classically hard problems. Although Boson sampling was initially considered a purely theoretical concept, it has practical applications such as calculating molecular vibronic spectra, 70,71 graph problems 72,73 …”

Section: Quantum Computing Preliminariesmentioning

confidence: 99%

Complexity of life sciences in quantum and AI era

Pyrkov,

Aliper,

Bezrukov

et al. 2024

WIREs Comput Mol Sci

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Having made significant advancements in understanding living organisms at various levels such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now shifting towards integrating these components into the bigger picture to understand their collective behavior. Such a shift of perspective requires a general conceptual framework for understanding complexity in life sciences which is currently elusive, a transition being facilitated by large‐scale data collection, unprecedented computational power, and new analytical tools. In recent years, life sciences have been revolutionized with AI methods, and quantum computing is touted to be the next most significant leap in technology. Here, we provide a theoretical framework to orient researchers around key concepts of how quantum computing can be integrated into the study of the hierarchical complexity of living organisms and discuss recent advances in quantum computing for life sciences.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Quantum Computing > Algorithms Structure and Mechanism > Computational Biochemistry and Biophysics

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A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation

Zhu,

Chen,

et al. 2023

Laser & Photonics Reviews

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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.

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Solving Graph Problems Using Gaussian Boson Sampling

Cited by 16 publications

References 38 publications

Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Complexity of life sciences in quantum and AI era

A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation

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