Quantum pattern recognition algorithms for charged particle tracking

Gray, H. M.

doi:10.1098/rsta.2021.0103

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…Penalty terms are ubiquitous in optimization problems and beyond, ranging from 0 -norm regularization terms to penalty terms enforcing physical constraints or symmetries (see Ref. [49] for an example of penalty terms in particle track reconstruction). While ground-state energies of generic Hamiltonians can be negative, penalty terms employ absolute values and thus vanish under minimization.…”

Section: Discussionmentioning

confidence: 99%

Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics

Anschuetz,

Funcke,

Komiske

et al. 2022

Preprint

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Classical and quantum annealing are computing paradigms that have been proposed to solve a wide range of optimization problems. In this paper, we aim to enhance the performance of annealing algorithms by introducing the technique of degeneracy engineering, through which the relative degeneracy of the ground state is increased by modifying a subset of terms in the objective Hamiltonian. We illustrate this novel approach by applying it to the example of 0-norm regularization for sparse linear regression, which is in general an NP-hard optimization problem. Specifically, we show how to cast 0-norm regularization as a quadratic unconstrained binary optimization (QUBO) problem, suitable for implementation on annealing platforms. As a case study, we apply this QUBO formulation to energy flow polynomials in high-energy collider physics, finding that degeneracy engineering substantially improves the annealing performance. Our results motivate the application of degeneracy engineering to a variety of regularized optimization problems.

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Section: Discussionmentioning

confidence: 99%

Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics

Anschuetz,

Funcke,

Komiske

et al. 2022

Preprint

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“…A review of various quantum computing algorithms studied for charged particle tracking can be found in Ref. [2].…”

Section: Methodsmentioning

confidence: 99%

Studying quantum algorithms for particle track reconstruction in the LUXE experiment

Funcke

Hartung²,

Heinemann³

et al. 2023

J. Phys.: Conf. Ser.

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The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in planning at DESY Hamburg, which will study Quantum Electrodynamics (QED) at the strong-field frontier. In this regime, QED is non-perturbative. This manifests itself in the creation of physical electron-positron pairs from the QED vacuum. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally very hard for classical computers. This paper presents a preliminary study to explore the potential of quantum computers to solve this problem and to reconstruct the positron trajectories from the detector energy deposits. The reconstruction problem is formulated in terms of a quadratic unconstrained binary optimisation. Finally, the results from the quantum simulations are discussed and compared with traditional classical track reconstruction algorithms.

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“…Artificial Neural Networks (ANNs) are the most advanced branch of machine learning. Deep learning techniques are used in a variety of applications, including speech recognition (Liang and Yan, 2022), pattern recognition (Gray, 2022), and bioinformatics (Yi et al, 2022). Deep learning systems have outperformed more standard machine learning methods in various fields.…”

Section: Classificationmentioning

confidence: 99%

A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks

Reddy¹,

P²

2022

Journal of Computer Science

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Skin cancer is a deadly malignancy. Incomplete D.N.A. repair in skin cells causes hereditary mutations and cancer. Early skin cancer is easier to treat since it spreads slowly to other body areas. As a result, the optimal time to find it is during its infancy. Because of the rising frequency of skin cancer, the high mortality rate, and the high cost of medical treatment, early detection of skin cancer symptoms is essential. Researchers have created a variety of early detection techniques for skin cancer due to these obstacles. A lesion's symmetry, coloration, size, and shape help doctors identify and differentiate between skin cancer and melanoma. These considerations prompted the researcher to do research into automated skin cancer diagnosis. The use of machine learning is quickly becoming one of the most promising approaches to the early detection and treatment of skin cancer. A recent study demonstrated the ability of deep network topologies to segment and analyzes skin cancer. According to the findings of this study, further investigation into the application of Deep Learning (DL) algorithms for the early detection of skin cancer is required. An investigation into significant research articles on skin cancer diagno sis that have been published in reputable journals was carried out.

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Quantum pattern recognition algorithms for charged particle tracking

Cited by 7 publications

References 28 publications

Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics

Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics

Studying quantum algorithms for particle track reconstruction in the LUXE experiment

A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks

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