Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Employing Integrated Quantum Photonic Computers for Gaussian Boson Sampling

Mehrdad Ghasemi, Hassan Kaatuzian, Houshyar Noshad, Mahmood Hassani, Mobin Motaharifar, Mahdi NoroozOliaei

Published May 14, 2024• 1 citation
Publisher

Abstract

Gaussian boson sampling (GBS) is a tool for demonstrating quantum computational utilities and also exhibits mathematical connections to specific graph-related and quantum chemistry problems. It has been suggested that the samples generated from GBS could be utilized to improve stochastic algorithms in searching for certain graph features. In this study, we employed X8, a noisy intermediate-scale quantum computer, to address graph problems. The samples were produced from a 4-mode quantum photonic processor. This research represents a step toward analyzing Gaussian boson sampling as a tool for solving real-world problems using existing noisy intermediate-scale quantum computers and aims to encourage the development of quantum algorithms.

DOI
10.1109/icee63041.2024.10667961

Related papers

Solving Graph Problems Using Gaussian Boson Sampling

Yu‐Hao Deng, Si-Qiu Gong, Yi-Chao Gu +21 more

Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhāng, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.

arXiv:2302.00936•Physical Review Letters•May 9, 2023•60 citations

Applications of Gaussian Boson Sampling to Solve Some Chemistry Problems

Samaneh Bagheri Novir

Quantum computers, due to their superposition and entanglement properties, provide significant advantages in solving certain problems compared with classical computers. Therefore, it is crucial to identify issues that can be efficiently solved by noisy intermediate-scale quantum (NISQ) systems. Xanadu has introduced the X8 quantum chip, based on integrated photonic technology, along with important photonic platforms such as Strawberry Fields and Gaussian Boson Sampling (GBS), to solve specific computational problems. In this review article, after reviewing Boson Sampling (BS) and Gaussian Boson Sampling (GBS), we discuss the relationship between GBS and graph theory, including how graphs can be encoded in GBS. Some applications of GBS, particularly molecular docking and molecular vibrations, are also considered. The future goal of this study is to identify problems that can be represented as small graphs and solved using GBS with a limited number of optical modes.

Quantum Reports•Nov 28, 2025•0 citations

Validation of a noisy Gaussian boson sampler via graph theory

Denis Stanev, Taira Giordani, Nicolò Spagnolo +1 more

Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage toward the realization of universal quantum computers. In the context of nonuniversal noisy intermediate quantum devices, photonic-based sampling machines solving the Gaussian boson sampling (GBS) problem currently play a central role in the experimental demonstration of quantum computational advantage. A relevant issue is the validation of the sampling process in the presence of experimental noise, such as photon losses, which could undermine the hardness of simulating the experiment. We test the capability of a validation protocol that exploits the connection between GBS and graph perfect match counting to perform such an assessment in a noisy scenario. In particular, we use as a test bench the recently developed machine Borealis, a large-scale sampling machine that has been made available online for external users, and address its operation in the presence of noise. The employed approach to validation is also shown to provide connections with the open question on the effective advantage of using noisy GBS devices for graph similarity and isomorphism problems and thus provides an effective method for certification of quantum hardware.

arXiv:2306.12120•Advanced Photonics Nexus•Jan 9, 2025•2 citations

Gaussian boson sampling: Benchmarking quantum advantage

Ned Goodman, Alexander S. Dellios, Margaret D. Reid +1 more

Quantum computers solve intractable problems which classically require an exponentially long time to compute. With the development of large-scale experiments that claim quantum advantage, a vital issue has now emerged. What are the errors, and how do they affect the complexity of the problem solved? Large-scale Gaussian boson sampling (GBS) experiments give an example in which random numbers are generated. Despite classical hardness, these have computable benchmarks for checking data validity. While there are other quantum computing architectures, Gaussian boson sampling is uniquely testable at all scales. Several large, pioneering quantum computing (QC) experiments have been carried out to investigate quantum advantage. Here, we introduce a highly scalable but classical algorithm that can solve GBS approximately. Our numerical simulation of the output count data is closer to the exact solution than current experiments up to 1152 modes. This algorithm outperforms all previous classical, approximate algorithms and scales efficiently to larger experiments. Our results show that effects beyond losses can cause the errors that allow classical simulability. This work will lead to more precise algorithms and is a step towards understanding how QC quantum advantage is affected by the underlying physics.

quant-pharXiv:2604.12330•Apr 15, 2026•0 citations

Probing Graph Similarity Through Multiple Photons in Frequency-Time Bin Modes Generated by a Silicon Nitride Microresonator

Massimo Borghi, Emanuele Brusaschi, Marco Liscidini +2 more

Boson-sampling problems are specific cases of multi-photon quantum interference in which the complexity of sampling from the output distribution of multiple photons on multiple modes arises from its relation to certain matrix functions that cannot be efficiently computed in polynomial time using the best-known classical algorithms. Specifically, gaussian boson sampling (GBS) is a protocol that employs Gaussian states as inputs to a linear interferometer, and whose output samples can be used to accelerate graph search and optimization problems [1]. To date, GBS experiments have been reported by using path or time-bin encoding of the optical modes.

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)•Jun 23, 2025•0 citations

A universal programmable Gaussian boson sampler for drug discovery

Shang Yu, Zhipeng Zhong, Yuhua Fang +21 more

Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.

arXiv:2210.14877•Nature Computational Science•Oct 12, 2023•50 citations
Gaussian boson sampling (GBS) is a tool for demonstrating quantum computational utilities and also exhibits mathematical connections to specific graph-related and quantum chemistry problems. It has been suggested that the samples generated from GBS could be utilized to improve stochastic algorithms in searching for certain graph features. In this study, we employed X8, a noisy intermediate-scale quantum computer, to address graph problems. The samples were produced from a 4-mode quantum photonic processor. This research represents a step toward analyzing Gaussian boson sampling as a tool for solving real-world problems using existing noisy intermediate-scale quantum computers and aims to encourage the development of quantum algorithms.
Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhāng, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.
Quantum computers, due to their superposition and entanglement properties, provide significant advantages in solving certain problems compared with classical computers. Therefore, it is crucial to identify issues that can be efficiently solved by noisy intermediate-scale quantum (NISQ) systems. Xanadu has introduced the X8 quantum chip, based on integrated photonic technology, along with important photonic platforms such as Strawberry Fields and Gaussian Boson Sampling (GBS), to solve specific computational problems. In this review article, after reviewing Boson Sampling (BS) and Gaussian Boson Sampling (GBS), we discuss the relationship between GBS and graph theory, including how graphs can be encoded in GBS. Some applications of GBS, particularly molecular docking and molecular vibrations, are also considered. The future goal of this study is to identify problems that can be represented as small graphs and solved using GBS with a limited number of optical modes.
Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage toward the realization of universal quantum computers. In the context of nonuniversal noisy intermediate quantum devices, photonic-based sampling machines solving the Gaussian boson sampling (GBS) problem currently play a central role in the experimental demonstration of quantum computational advantage. A relevant issue is the validation of the sampling process in the presence of experimental noise, such as photon losses, which could undermine the hardness of simulating the experiment. We test the capability of a validation protocol that exploits the connection between GBS and graph perfect match counting to perform such an assessment in a noisy scenario. In particular, we use as a test bench the recently developed machine Borealis, a large-scale sampling machine that has been made available online for external users, and address its operation in the presence of noise. The employed approach to validation is also shown to provide connections with the open question on the effective advantage of using noisy GBS devices for graph similarity and isomorphism problems and thus provides an effective method for certification of quantum hardware.
Quantum computers solve intractable problems which classically require an exponentially long time to compute. With the development of large-scale experiments that claim quantum advantage, a vital issue has now emerged. What are the errors, and how do they affect the complexity of the problem solved? Large-scale Gaussian boson sampling (GBS) experiments give an example in which random numbers are generated. Despite classical hardness, these have computable benchmarks for checking data validity. While there are other quantum computing architectures, Gaussian boson sampling is uniquely testable at all scales. Several large, pioneering quantum computing (QC) experiments have been carried out to investigate quantum advantage. Here, we introduce a highly scalable but classical algorithm that can solve GBS approximately. Our numerical simulation of the output count data is closer to the exact solution than current experiments up to 1152 modes. This algorithm outperforms all previous classical, approximate algorithms and scales efficiently to larger experiments. Our results show that effects beyond losses can cause the errors that allow classical simulability. This work will lead to more precise algorithms and is a step towards understanding how QC quantum advantage is affected by the underlying physics.
Boson-sampling problems are specific cases of multi-photon quantum interference in which the complexity of sampling from the output distribution of multiple photons on multiple modes arises from its relation to certain matrix functions that cannot be efficiently computed in polynomial time using the best-known classical algorithms. Specifically, gaussian boson sampling (GBS) is a protocol that employs Gaussian states as inputs to a linear interferometer, and whose output samples can be used to accelerate graph search and optimization problems [1]. To date, GBS experiments have been reported by using path or time-bin encoding of the optical modes.
Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.