Accelerating Neural Network Ensemble Learning Using Optimization and Quantum Annealing Techniques

Jooya, Ali; Keshavarz, Babak; Dimopoulos, N.J.; Oberoi, Jaspreet S.

doi:10.1145/3149526.3149528

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Many problems can be formulated to take advantage of quantum annealing, and is advantageous because it converges faster than other techniques to an optimum solution [10].…”

Section: B Quantum Annealingmentioning

confidence: 99%

Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging

Kelany

Dimopoulos

Adolphs³

et al. 2022

IEEE Trans. Quantum Eng.

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“…Many problems can be formulated to take advantage of quantum annealing, and is advantageous because it converges faster than other techniques to an optimum solution [10].…”

Section: B Quantum Annealingmentioning

confidence: 99%

Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging

Kelany

Dimopoulos

Adolphs³

et al. 2022

IEEE Trans. Quantum Eng.

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“…Many problems can be formulated to take advantage of quantum annealing, which is advantageous because it converges faster than other techniques to an optimum solution [10].…”

Section: B Quantum Annealingmentioning

confidence: 99%

Quantum Annealing Approaches to the Phase-Unwrapping Problem in Synthetic-Aperture Radar Imaging

Kelany¹,

Dimopoulos

Adolphs³

et al. 2020

2020 IEEE International Conference on Quantum Computing and Engineering (QCE)

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The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger-sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that can be solved individually. These individual solutions are close to optimal up to an integer constant, with one constant per sub-image. In a second phase, these integer constants are determined as a solution to yet another QUBO problem. We test our approach with a variety of software-based QUBO solvers and on a variety of images, both synthetic and real. Additionally, we experiment using D-Wave Systemss quantum annealer, the D-Wave 2000Q. The software-based solvers obtain high-quality solutions comparable to state-of-the-art phase-unwrapping solvers. We are currently working on optimally mapping the problem onto the restricted topology of the quantum annealer to improve the quality of the solution.

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“…However, in low to moderate dimensions, PDE solvers based on NNs or deep NNs typically fall short when compared to classical numerical solution methods. Tis is primarily because solving an algebraic equation is generally easier than dealing with the highly non-linear, large-scale optimization problems associated with NN training [49,50].…”

Section: Introductionmentioning

confidence: 99%

Exploring Physics‐Informed Neural Networks for the Generalized Nonlinear Sine‐Gordon Equation

Deresse,

Dufera

2024

Applied Computational Intelligence and Soft Computing

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The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation, encompassing Dirichlet and Neumann boundary conditions. To incorporate physical information for the sine-Gordon equation, a multiobjective loss function has been defined consisting of the residual of governing partial differential equation (PDE), initial conditions, and various boundary conditions. Using multiple densely connected independent artificial neural networks (ANNs), called feedforward deep neural networks designed to handle partial differential equations, PINNs have been trained through automatic differentiation to minimize a loss function that incorporates the given PDE that governs the physical laws of phenomena. To illustrate the effectiveness, validity, and practical implications of our proposed approach, two computational examples from the nonlinear sine-Gordon are presented. We have developed a PINN algorithm and implemented it using Python software. Various experiments were conducted to determine an optimal neural architecture. The network training was employed by using the current state-of-the-art optimization methods in machine learning known as Adam and L-BFGS-B minimization techniques. Additionally, the solutions from the proposed method are compared with the established analytical solutions found in the literature. The findings show that the proposed method is a computational machine learning approach that is accurate and efficient for solving nonlinear sine-Gordon equations with a variety of boundary conditions as well as any complex nonlinear physical problems across multiple disciplines.

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Accelerating Neural Network Ensemble Learning Using Optimization and Quantum Annealing Techniques

Cited by 6 publications

References 18 publications

Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging

Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging

Quantum Annealing Approaches to the Phase-Unwrapping Problem in Synthetic-Aperture Radar Imaging

Exploring Physics‐Informed Neural Networks for the Generalized Nonlinear Sine‐Gordon Equation

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