A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan, Anh-Dzung; Sasdelli, Michele; Suter, David; Chin, Tat-Jun

doi:10.1109/cvpr52688.2022.00051

Cited by 16 publications

(14 citation statements)

References 41 publications

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“…Hybrid Quantum Classical Approach to Information Security Specific Task. We extracted information from [9], [20], [21], [24] and [19] to elucidate on their particular information security-related task in a classical-quantum environment. reliance on digital systems and the risk of cyberattacks; therefore, hybrid environments, such as QML, can be a potential solution for achieving higher levels of accuracy.…”

Section: Hybrid Approaches To Information Securitymentioning

confidence: 99%

Toward Hybrid Classical Deep Learning-Quantum Methods for Steganalysis

Wardhani,

Putranto,

et al. 2024

IEEE Access

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This paper explores the potential of the hybrid classical deep learning quantum in addressing contemporary information security challenges, including cyber-attack detection and prevention, botnet detection, IP-theft, hardware defect detection, and more, with a specific emphasis on steganalysis. We begin by offering a succinct overview of current information security research trends, focusing on the hybrid classical deep learning-quantum paradigm. Subsequently, we introduce a classical-quantum hybrid framework tailored for uncovering concealed information within a significant public steganalysis dataset, ALASKA2. To enable image extraction and classification, we present a two-layer architecture that integrates convolutional neural network layers into both the classical deep learning and quantum layers. Notably, the proposed approach has improved steganalysis accuracy by achieving a 97% implementation accuracy rate.

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Section: Hybrid Approaches To Information Securitymentioning

confidence: 99%

Toward Hybrid Classical Deep Learning-Quantum Methods for Steganalysis

Wardhani,

Putranto,

et al. 2024

IEEE Access

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“…However, QA only accepts a quadratic polynomial over binary variables, and the cost of using a large number of ancillary qubits to represent the discretized variables is expensive. Most prior works formulate the real-world application as a binary quadratic model (BQM) problem [21]- [25] or mixed-integer linear programming (MILP) problem [28], [29] that is easily converted to the QUBO formulation for QA to optimize. For instance, Yarkoni et al [21] minimized the number of color switches between cars in a paint shop queue by formulating a BQM problem.…”

Section: B Quantum Annealingmentioning

confidence: 99%

“…Mugel et al [25] proposed a hybrid quantum-classical algorithm for dynamic portfolio optimization with a minimum holding period. Doan et al [28] studied MILP problem in robust fitting by leveraging QA. Dinh et al [29] formulated the beam placement in satellite communication as an MILP and designed an efficient Hamiltonian Reduction method for QA to address this problem efficiently.…”

Section: B Quantum Annealingmentioning

confidence: 99%

Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

Zhang

Liu

et al. 2023

IEEE Trans. Wireless Commun.

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Low earth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN) architecture to provide ground users with seamless and reliable content delivery services. For optimal service provisioning in this architecture, we formulate an optimization model to maximize the network throughput by jointly optimizing content delivery policy, cache placement, and transmission power allocation. The resulting optimization model is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computer solvers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithm to address this challenge. Specifically, we first exploit the generalized Benders' decomposition (GBD) to decompose the problem into a master problem and a subproblem and then leverage the state-of-art quantum annealer to solve the challenging master problem. Furthermore, a multi-cut strategy is designed in HQCGBD to accelerate the solution process by leveraging the quantum advantages in parallel computing. Simulation results demonstrate the superiority of the proposed HQCGBD algorithm and validate the effectiveness of the proposed cache-enabled ISTN architecture.

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“…Quantum Computer Vision (QCV). Several algorithms for computer vision relying on quantum hardware were proposed over the last three years for such problems as shape matching [23,35,42], object tracking [32,47], fundamental matrix estimation, point triangulation [20] and motion segmentation [1], among others. The majority of them address various types of alignment problems, i.e., transformation estimation [23,35], point set [23,37] and mesh alignment [42], graph matching [3,41] and permutation synchronisation [3].…”

Section: Related Workmentioning

confidence: 99%

CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes

Bhatia¹,

Tretschk²,

Lähner³

et al. 2023

Preprint

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Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, N P-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum shape-matching methods do not support multiple shapes and even less cycle consistency. This paper addresses the open challenges and introduces the first quantum-hybrid approach for 3D shape multimatching; in addition, it is also cycle-consistent. Its iterative formulation is admissible to modern adiabatic quantum hardware and scales linearly with the total number of input shapes. Both these characteristics are achieved by reducing the N -shape case to a sequence of three-shape matchings, the derivation of which is our main technical contribution. Thanks to quantum annealing, high-quality solutions with low energy are retrieved for the intermediate N Phard objectives. On benchmark datasets, the proposed approach significantly outperforms extensions to multi-shape matching of a previous quantum-hybrid two-shape matching method and is on-par with classical multi-matching methods. Our source code is available at 4dqv.mpiinf.mpg.de/CCuantuMM/.

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A Hybrid Quantum-Classical Algorithm for Robust Fitting

Cited by 16 publications

References 41 publications

Toward Hybrid Classical Deep Learning-Quantum Methods for Steganalysis

Toward Hybrid Classical Deep Learning-Quantum Methods for Steganalysis

Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes

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