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Journal articles on the topic 'Quantum-based thresholding'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wang, Xiangluo, Chunlei Yang, Guo-Sen Xie, and Zhonghua Liu. "Image Thresholding Segmentation on Quantum State Space." Entropy 20, no. 10 (2018): 728. http://dx.doi.org/10.3390/e20100728.

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Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searchi

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2

Xu, Aidong, Wenqi Huang, Peng Li, Huajun Chen, Jiaxiao Meng, and Xiaobin Guo. "Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model." Shock and Vibration 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5169070.

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Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maxi

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3

Ge, Yangyang, Zhimin Wang, Wen Zheng, et al. "Optimized quantum singular value thresholding algorithm based on a hybrid quantum computer." Chinese Physics B 31, no. 4 (2022): 048704. http://dx.doi.org/10.1088/1674-1056/ac40fb.

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Quantum singular value thresholding (QSVT) algorithm, as a core module of many mathematical models, seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors. The existing all-qubit QSVT algorithm demands lots of ancillary qubits, remaining a huge challenge for realization on near-term intermediate-scale quantum computers. In this paper, we propose a hybrid QSVT (HQSVT) algorithm utilizing both discrete variables (DVs) and continuous variables (CVs). In our algorithm, raw data vectors are encoded into a qubit system and the following

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4

Zhang, Jian, Huanzhou Li, Zhangguo Tang, Qiuping Lu, Xiuqing Zheng, and Jiliu Zhou. "An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/295402.

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A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. Th

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5

Cao, Lian Lian, Sheng Ding, Xiao Wei Fu, and Li Chen. "Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm." International Journal of Wireless and Mobile Computing 10, no. 3 (2016): 272. http://dx.doi.org/10.1504/ijwmc.2016.077215.

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6

Li, Aoqing, Fan Li, Qidi Gan, and Hongyang Ma. "Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder." Applied Sciences 13, no. 17 (2023): 9689. http://dx.doi.org/10.3390/app13179689.

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Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topolo

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7

Yang, Zhenlun, and Angus Wu. "A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation." Neural Computing and Applications 32, no. 16 (2019): 12011–31. http://dx.doi.org/10.1007/s00521-019-04210-z.

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8

Li, Yangyang, Xiaoyu Bai, Licheng Jiao, and Yu Xue. "Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation." Applied Soft Computing 56 (July 2017): 345–56. http://dx.doi.org/10.1016/j.asoc.2017.03.018.

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9

Pai, A. G., K. M. Buddhiraju, and S. S. Durbha. "QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W6-2022 (February 23, 2023): 483–88. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-483-2023.

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Abstract. Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali’s,

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10

Sindugatta Nagaraja, Prajwalasimha, Naveen Kulkarani, Raghavendra M. Ichangi, et al. "QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1959–68. https://doi.org/10.11591/eei.v14i3.9036.

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This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise.

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11

Li, Yangyang, Licheng Jiao, Ronghua Shang, and Rustam Stolkin. "Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation." Information Sciences 294 (February 2015): 408–22. http://dx.doi.org/10.1016/j.ins.2014.10.005.

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12

Lee, Kim F., Daniel R. Reilly, Paul Moraw, and Gregory S. Kanter. "Emulation of up-conversion based quantum key distribution scheme using active pump-controlled basis selection and adaptive thresholding." Optics Communications 475 (November 2020): 126258. http://dx.doi.org/10.1016/j.optcom.2020.126258.

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13

Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding." Swarm and Evolutionary Computation 15 (April 2014): 38–57. http://dx.doi.org/10.1016/j.swevo.2013.11.002.

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14

Wang, Yi, Chuannuo Xu, Yu Wang, and Xuezhen Cheng. "A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM." Entropy 23, no. 9 (2021): 1142. http://dx.doi.org/10.3390/e23091142.

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A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was prese

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15

Mohseninia, Razieh, Jing Yang, Irfan Siddiqi, Andrew N. Jordan, and Justin Dressel. "Always-On Quantum Error Tracking with Continuous Parity Measurements." Quantum 4 (November 4, 2020): 358. http://dx.doi.org/10.22331/q-2020-11-04-358.

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We investigate quantum error correction using continuous parity measurements to correct bit-flip errors with the three-qubit code. Continuous monitoring of errors brings the benefit of a continuous stream of information, which facilitates passive error tracking in real time. It reduces overhead from the standard gate-based approach that periodically entangles and measures additional ancilla qubits. However, the noisy analog signals from continuous parity measurements mandate more complicated signal processing to interpret syndromes accurately. We analyze the performance of several practical fi

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16

A., Sathish, and Aravind Babu L.R. "Analyzing the Efficiency of Algorithm for Routing and Data Transmission in Wireless Sensor Networks." ACCST RESEARCH JOURNAL XX, no. 2, April 2022 (2022): 26–35. https://doi.org/10.5281/zenodo.7310104.

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                  Wireless Sensor Networks (WSNs) comprises a massive set of sensor nodes undergo random deployment in the target region to observe the physical parameters. The sensor nodes in WSN are constrained in energy, transmission power, storage, and computation abilities. Several studies ensured that the energy spent for data transmission is significantly higher than sensing and processing.  Design of effective routing mechanism in WSN finds helpful to reduce the energy consumption in throughout the network. Several approaches are availa

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17

Prajwalasimha, Sindugatta Nagaraja, Kulkarni Naveen, M. Ichangi Raghavendra, et al. "QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems." May 16, 2025. https://doi.org/10.11591/eei.v14i3.9036.

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This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise.

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18

Bao, Fanglin, leif bauer, adrian Rubio Lopez, Ziyi Yang, Xueji Wang, and Zubin Jacob. "Photon discerner: Adaptive quantum optical sensing near the shot noise limit." New Journal of Physics, July 19, 2024. http://dx.doi.org/10.1088/1367-2630/ad6584.

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Abstract Photon statistics of an optical ﬁeld can be used for quantum optical sensing in low light level scenarios free of bulky optical components. However, photon-number-resolving detection to unravel the photon statistics is challenging. Here, we propose a novel detection approach, that we call ‘photon discerning’, which uses adaptive photon thresholding for photon statistical estimation without recording exact photon numbers. Our photon discerner is motivated by the ﬁeld of neural networks where tunable thresholds have proven eﬃcient for information extraction in machine learning tasks. Th

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19

Sayed, Gehad Ismail. "A novel multilevel thresholding algorithm based on quantum computing for abdominal CT liver images." Evolutionary Intelligence, October 5, 2021. http://dx.doi.org/10.1007/s12065-021-00669-9.

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20

Gizachew Yirga, Tsehayu, Hailu Gizachew Yirga, and Eshetie Gizachew Addisu. "Cryptographic key generation using deep learning with biometric face and finger vein data." Frontiers in Artificial Intelligence 8 (April 29, 2025). https://doi.org/10.3389/frai.2025.1545946.

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This research proposes a novel approach to cryptographic key generation using biometric data from face and finger vein modalities enhanced by deep learning techniques. Using pretrained models FaceNet and VGG19 for feature extraction and employing a Siamese Neural Network (SNN), the study demonstrates the integration of multimodal biometrics with fuzzy extractors to create secure and reproducible cryptographic keys. Feature fusion techniques, combined with preprocessing and thresholding, ensure robust feature extraction and conversion to binary formats for key generation. The model demonstrates

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21

Deng, Zi-Lan, Meng-Xia Hu, Shanfeng Qiu, et al. "Poincaré sphere trajectory encoding metasurfaces based on generalized Malus’ law." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-46758-y.

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AbstractAs a fundamental property of light, polarization serves as an excellent information encoding carrier, playing significant roles in many optical applications, including liquid crystal displays, polarization imaging, optical computation and encryption. However, conventional polarization information encoding schemes based on Malus’ law usually consider 1D polarization projections on a linear basis, implying that their encoding flexibility is largely limited. Here, we propose a Poincaré sphere (PS) trajectory encoding approach with metasurfaces that leverages a generalized form of Malus’ l

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22

Vayadande, Kuldeep, Yogesh Bodhe, Amol A. Bhosle, et al. "Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection." EAI Endorsed Transactions on Internet of Things 11 (April 30, 2025). https://doi.org/10.4108/eetiot.8170.

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This research investigates the use of quantum machine learning (QML) to classify asteroids into non-hazardous and hazardous groups, which yields successful results in detecting the hazard. In addition to the complexity involved in analyzing orbits and physical objects, QML performs better than traditional machine learning in modeling the relationship between data. This method involves data-intensive preprocessing steps, such as feature selection by removing unnecessary features and correlation analysis to find predictors. Quantum circuits are used for specification and classification, and the

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23

Yen, Meng‐Cheng, Yung‐Chi Yao, Chia‐Jung Lee, et al. "Color‐Filter‐Free Image Sensor Using CsPbBr₃ Quantum‐Dot‐Based Tamm Plasmon Photodetector for Photonic Synapse Facial Recognition." Advanced Science, June 25, 2025. https://doi.org/10.1002/advs.202503464.

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AbstractThis study demonstrates that optical absorption of the CsPbBr3 quantum dots can be significantly enhanced through monolithic integration with the Tamm plasmon (TP) structure. This integration enables the resulting TP photodetector to achieve a higher photocurrent and a more linear power dependence compared to the reference device with a nonresonant configuration. The enhancement is confined to the designed resonant energy, while photons with off‐resonance energies are fully reflected, making the TP photodetector an ideal candidate for compact and efficient image sensors, eliminating th

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24

Karamuftuoglu, Mustafa Altay, Beyza Zeynep Ucpinar, Sasan Razmkhah, Arash Fayyazi, Mehdi Kamal, and Massoud Pedram. "Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware." Superconductor Science and Technology, January 15, 2025. https://doi.org/10.1088/1361-6668/adaaa9.

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Abstract A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with more synaptic connections, enhancing the overall network capabilities. The proposed neuron design is based on superconductor electronics fabric, incorporating multiple superconducting loops, each with two Josephson Junctions. This arrangement enables each input data branch to have positive and negative inductive coupling, supporting excitatory and i

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