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

Chip

Time-domain-multiplexed Gaussian Boson sampling for graph problems in finance

Shuo He, Boxuan Ai, Pengfei Gao, Hongbao Liu, Jun-Jie He, Ke-ming Hu, Yuze Zhu, Jie Wu

Published Dec 24, 2025• 0 citations
Publisher

Abstract

Gaussian Boson sampling (GBS) has emerged as a promising platform for demonstrating quantum computational advantage. Here, we apply GBS to address classically intractable graph problems in financial security. We demonstrate algorithms for: (1) Community structure analysis for market anomaly screening; (2) Synthetic identity fraud detection via hierarchical dense subgraph identification; (3) Collusive stock manipulation detection through clique enumeration; and (4) Anti-money laundering through isomorphic subgraph matching. Notably, we design non-universal time-domain-multiplexed photonic quantum circuits to effectively map these GBS-based algorithms with a quadratic reduction in hardware complexity, achieving over 99% fidelity in fitting the target unitary matrix. This spatial-temporal hybrid architecture and a hardware-algorithm co-design framework establish a promising approach to complex financial graph analytics. Our work bridges the fields of quantum computing and finance, highlighting GBS as a near-term solution to critical industry challenges.

DOI
10.1016/j.chip.2025.100181

Related papers

Gaussian Boson Sampling for Asset Clustering in Statistical Arbitrage Portfolios

Dayne Marcus Lopena, Daniel Buguks, Zhenghao Li +7 more

Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems. Simultaneously, correlation matrix clustering algorithms, such as Spectral and SPONGE, have established robust benchmarks for identifying co-moving assets from correlation matrices in statistical arbitrage (StatArb) strategies. In this work, we map S&P 500 residual correlation data into GBS-compatible adjacency matrices. We benchmark those classical clustering algorithms against two quantum clustering algorithms, GBS Boost and our novel GBS Roots, to construct dynamic, market-neutral portfolios over a rolling one-year window. Simulations across distinct macroeconomic regimes reveal that quantum clustering generates superior alpha within large stock universes during periods of high volatility, effectively isolating structural market idiosyncrasies. Crucially, this economic advantage persists under simulated low-loss conditions and extends into high-loss regimes via the application of coherent displacement to compensate for photon loss. Our findings underscore the efficacy of GBS-derived graph clustering in constructing robust StatArb portfolios, establishing a quantum foundation for broader quantitative finance applications.

quant-pharXiv:2607.19279•Jul 22, 2026•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

A Quantum Photonic Approach to Graph Coloring

Jesua Epequin, Pascale Bendotti, Joseph Mikael

Gaussian Boson Sampling (GBS) is a quantum computational model that leverages linear optics to solve sampling problems believed to be classically intractable. Recent experimental breakthroughs have demonstrated quantum advantage using GBS, motivating its application to real-world combinatorial optimization problems. In this work, we reformulate the graph coloring problem as an integer programming problem using the independent set formulation. This enables the use of GBS to identify cliques in the complement graph, which correspond to independent sets in the original graph. Our method is benchmarked against classical heuristics and exact algorithms on two sets of instances: Erdős-Rényi random graphs and graphs derived from a smart-charging use case. The results demonstrate that GBS can provide competitive solutions, highlighting its potential as a quantum-enhanced heuristic for graph-based optimization.

quant-pharXiv:2601.20263•Jan 29, 2026•0 citations

Using Gaussian Boson Sampling to Find Dense Subgraphs

Juan Miguel Arrazola, Thomas R. Bromley

Boson sampling devices are a prime candidate for exhibiting quantum supremacy, yet their application for solving problems of practical interest is less well understood. Here we show that Gaussian boson sampling (GBS) can be used for dense subgraph identification. Focusing on the NP-hard densest k-subgraph problem, we find that stochastic algorithms are enhanced through GBS, which selects dense subgraphs with high probability. These findings rely on a link between graph density and the number of perfect matchings-enumerated by the Hafnian-which is the relevant quantity determining sampling probabilities in GBS. We test our findings by constructing GBS-enhanced versions of the random search and simulated annealing algorithms and apply them through numerical simulations of GBS to identify the densest subgraph of a 30 vertex graph.

arXiv:1803.10730•Physical Review Letters•Jul 19, 2018•140 citations

Towards a Hybrid Quantum Enhanced Solution for Densest k-Subgraph Problem

Ravi Sangwan, Prabhat Anand, M Girish Chandra

We study the application of Gaussian Boson Sampling (GBS) to the densest k-subgraph problem (DkSP). GBS with hard post-selection suffers from poor sampling efficiency due to strict cardinality constraints. To address this limitation, we introduce effective classical post-processing strategies that transform, otherwise discarded, near-k samples into feasible solutions. A comprehensive set of simulations is carried out, demonstrating that these approaches achieve near-optimal solution quality while improving sampling efficiency by approximately 4X compared to post-selection on community-structured graphs, and also post-selection often fails to reach the optimal solution on sparse random graphs even with large number of samples. Furthermore, the proposed methods perform on par with, and in some cases outperform, established classical approaches for graphs up to moderate size. Overall, the results indicate that while GBS with post-selection alone is insufficient, its combination with lightweight classical refinement can be highly effective. This underscores the potential of hybrid quantum-classical frameworks and positions GBS as a promising sampling primitive for combinatorial graph optimization.

quant-pharXiv:2606.03196•Jun 3, 2026•0 citations

Experimentally Finding Dense Subgraphs Using a Time-Bin Encoded Gaussian Boson Sampling Device

S. Sempere-Llagostera, Raj B. Patel, Ian A. Walmsley +1 more

Gaussian boson sampling is a quantum computing concept based on drawing samples from a multimode nonclassical Gaussian state using photon-number resolving detectors.It was initially posed as a near-term approach to achieve quantum advantage, and several applications have been proposed since, including the calculation of graph features.For the first time, we use a time-bin encoded interferometer to implement Gaussian boson sampling experimentally and extract samples to enhance the search for dense subgraphs in a graph.Our results indicate an improvement over classical methods for subgraphs of sizes three and four in a graph containing ten nodes.In addition, we numerically explore the role of imperfections in the optical circuit and on the performance of the algorithm.

arXiv:2204.05254•Physical Review X•Sep 30, 2022•20 citations
Gaussian Boson sampling (GBS) has emerged as a promising platform for demonstrating quantum computational advantage. Here, we apply GBS to address classically intractable graph problems in financial security. We demonstrate algorithms for: (1) Community structure analysis for market anomaly screening; (2) Synthetic identity fraud detection via hierarchical dense subgraph identification; (3) Collusive stock manipulation detection through clique enumeration; and (4) Anti-money laundering through isomorphic subgraph matching. Notably, we design non-universal time-domain-multiplexed photonic quantum circuits to effectively map these GBS-based algorithms with a quadratic reduction in hardware complexity, achieving over 99% fidelity in fitting the target unitary matrix. This spatial-temporal hybrid architecture and a hardware-algorithm co-design framework establish a promising approach to complex financial graph analytics. Our work bridges the fields of quantum computing and finance, highlighting GBS as a near-term solution to critical industry challenges.
Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems. Simultaneously, correlation matrix clustering algorithms, such as Spectral and SPONGE, have established robust benchmarks for identifying co-moving assets from correlation matrices in statistical arbitrage (StatArb) strategies. In this work, we map S&P 500 residual correlation data into GBS-compatible adjacency matrices. We benchmark those classical clustering algorithms against two quantum clustering algorithms, GBS Boost and our novel GBS Roots, to construct dynamic, market-neutral portfolios over a rolling one-year window. Simulations across distinct macroeconomic regimes reveal that quantum clustering generates superior alpha within large stock universes during periods of high volatility, effectively isolating structural market idiosyncrasies. Crucially, this economic advantage persists under simulated low-loss conditions and extends into high-loss regimes via the application of coherent displacement to compensate for photon loss. Our findings underscore the efficacy of GBS-derived graph clustering in constructing robust StatArb portfolios, establishing a quantum foundation for broader quantitative finance applications.
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.
Gaussian Boson Sampling (GBS) is a quantum computational model that leverages linear optics to solve sampling problems believed to be classically intractable. Recent experimental breakthroughs have demonstrated quantum advantage using GBS, motivating its application to real-world combinatorial optimization problems. In this work, we reformulate the graph coloring problem as an integer programming problem using the independent set formulation. This enables the use of GBS to identify cliques in the complement graph, which correspond to independent sets in the original graph. Our method is benchmarked against classical heuristics and exact algorithms on two sets of instances: Erdős-Rényi random graphs and graphs derived from a smart-charging use case. The results demonstrate that GBS can provide competitive solutions, highlighting its potential as a quantum-enhanced heuristic for graph-based optimization.
Boson sampling devices are a prime candidate for exhibiting quantum supremacy, yet their application for solving problems of practical interest is less well understood. Here we show that Gaussian boson sampling (GBS) can be used for dense subgraph identification. Focusing on the NP-hard densest k-subgraph problem, we find that stochastic algorithms are enhanced through GBS, which selects dense subgraphs with high probability. These findings rely on a link between graph density and the number of perfect matchings-enumerated by the Hafnian-which is the relevant quantity determining sampling probabilities in GBS. We test our findings by constructing GBS-enhanced versions of the random search and simulated annealing algorithms and apply them through numerical simulations of GBS to identify the densest subgraph of a 30 vertex graph.
We study the application of Gaussian Boson Sampling (GBS) to the densest k-subgraph problem (DkSP). GBS with hard post-selection suffers from poor sampling efficiency due to strict cardinality constraints. To address this limitation, we introduce effective classical post-processing strategies that transform, otherwise discarded, near-k samples into feasible solutions. A comprehensive set of simulations is carried out, demonstrating that these approaches achieve near-optimal solution quality while improving sampling efficiency by approximately 4X compared to post-selection on community-structured graphs, and also post-selection often fails to reach the optimal solution on sparse random graphs even with large number of samples. Furthermore, the proposed methods perform on par with, and in some cases outperform, established classical approaches for graphs up to moderate size. Overall, the results indicate that while GBS with post-selection alone is insufficient, its combination with lightweight classical refinement can be highly effective. This underscores the potential of hybrid quantum-classical frameworks and positions GBS as a promising sampling primitive for combinatorial graph optimization.
Gaussian boson sampling is a quantum computing concept based on drawing samples from a multimode nonclassical Gaussian state using photon-number resolving detectors.It was initially posed as a near-term approach to achieve quantum advantage, and several applications have been proposed since, including the calculation of graph features.For the first time, we use a time-bin encoded interferometer to implement Gaussian boson sampling experimentally and extract samples to enhance the search for dense subgraphs in a graph.Our results indicate an improvement over classical methods for subgraphs of sizes three and four in a graph containing ten nodes.In addition, we numerically explore the role of imperfections in the optical circuit and on the performance of the algorithm.