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Journal articles on the topic 'Quantum-based thresholding'

Author: Grafiati

Published: 2 June 2025

Last updated: 19 July 2025

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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 searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods.

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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-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.

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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 data processing is fulfilled by hybrid quantum operations. Our algorithm requires O[log(MN)] qubits with O(1) qumodes and totally performs O(1) operations, which significantly reduces the space and runtime consumption.

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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. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

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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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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 topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.

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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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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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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, DE-GLCM, and three different variants of QGA, namely- QGA-Otsu, QGA-Kapur, QGA-Tsali’s. Intensity value from image pixel is the only information used by Otsu, Tsali’s and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.

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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. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.

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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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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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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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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 presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.

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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 filtering methods for continuous error correction and demonstrate that they are viable alternatives to the standard ancilla-based approach. As an optimal filter, we discuss an unnormalized (linear) Bayesian filter, with improved computational efficiency compared to the related Wonham filter introduced by Mabuchi [New J. Phys. 11, 105044 (2009)]. We compare this optimal continuous filter to two practical variations of the simplest periodic boxcar-averaging-and-thresholding filter, targeting real-time hardware implementations with low-latency circuitry. As variations, we introduce a non-Markovian ``half-boxcar'' filter and a Markovian filter with a second adjustable threshold; these filters eliminate the dominant source of error in the boxcar filter, and compare favorably to the optimal filter. For each filter, we derive analytic results for the decay in average fidelity and verify them with numerical simulations.

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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 available with the objective of enabling energy efficient routing to maximize the network lifetime. Energy efficiency remains a major issue in the design of wireless sensor networks (WSN). Earlier studies reported that the routing techniques can be employed to reduce energy consumption and lengthen the lifetime of WSN. Numerous routing techniques with dissimilar features are proposed in the literature to design an energy-efficient WSN. The Oppositional Lion Optimization algorithm (OLOA) based routing technique called OLOA-R to select the optimal routes based on energy consumption, residual energy, and distance between nodes. The proposed algorithm has been simulated and the simulation outcome shows the improved performance over the compared methods in a significant way. The BSO algorithm is presented to enhance the effectiveness of the swarm optimization by the foraging principle of beetles. The presented model chooses the possible routes to destination based on the fitness function involving residual energy, distance to base station (BS), and node degree. Besides, the presented model follows hybrid data transmission process where the data is transmitted at periodic time duration and reactive way. Moreover, a thresholding mechanism is applied for reactive data transmission. This process helps to balance the load properly and thereby achieves energy efficiency.

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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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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. The photon discerner maximizes Fisher information per photon by iteratively choosing the optimal threshold in real-time to approach the shot noise limit. Our proposed scheme of adaptive photon thresholding leads to unique remote-sensing applications of quantum DoLP (degree of linear polarization) camera and quantum LiDAR. We investigate optimal thresholds and show that the optimal photon threshold can be counter-intuitive (not equal to 1) even for weak signals (mean photon number much less than 1), due to the photon bunching eﬀect. We also put forth a superconducting nanowire realization of the photon discerner which can be experimentally implemented in the near-term. We show that the adaptivity of our photon discerner enables it to beat realistic photon-number-resolving detectors with limited photon-number resolution. Our work suggests a new class of detectors for information-theory driven, compact, and learning-based quantum optical sensing.&#xD;

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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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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 impressive accuracy with a vector converter, achieving a sigma similarity of 93% and a sigma difference of 64.0%. Evaluation metrics, including False Acceptance Rate (FAR) and False Rejection Rate (FRR), indicate significant improvements, achieving FRR &lt; 3.4% and FAR &lt; 1%, outperforming previous works. Additionally, the adoption of Goppa code-based cryptographic systems ensures post-quantum security. This study not only enhances biometric cryptography’s accuracy and resilience but also paves the way for future exploration of quantum-resistant and scalable systems.

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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’ law governing universal 2D projections between arbitrary elliptical polarization pairs spanning the entire PS. Arbitrary polarization encodings are realized by engineering PS trajectories governed by either arbitrary analytic functions or aligned modulation grids of interest, leading to versatile polarization image transformation functionalities, including histogram stretching, thresholding and image encryption within non-orthogonal PS loci. Our work significantly expands the encoding dimensionality of polarization information, unveiling new opportunities for metasurfaces in polarization optics for both quantum and classical regimes.

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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 standard evaluation is based on accuracy, recall, F1 score, and precision. A strong and weak method is provided by cross-validation and hyperparameter tuning. The best classical model here is the decision tree, which is a good model for high-resolution and low-budget social benefit with 0.883 accuracy, 0.955 recovery rate, 0.981 F1 score, and 0.883 sensitivity. However, the quantum model has made great leaps. AMSGRAD QCNN (Adaptive Moment Estimation with Gradient Thresholding Quantum Convolutional Neural Networks) achieves a non-uniform accuracy of 0.997, which is 13% higher than the decision tree with 0.984 accuracy, 0.955 recovery rate, and 0.981 F1 score. The accuracy of SPSA QCNN(Simultaneous Perturbation Stochastic Approximation) is 0.993, the recall is 0.974, and the F1 score is 0.977. This improvement shows the excellent ability of the quantum model to better resolve correlation data and, more importantly, to reduce false negatives in detecting stars. These results demonstrate the ability of quantum computing to analyze complex data and provide the best results. Our future work will focus on identifying and repairing quantum circuits, as well as exploring hybrid quantum classical models to improve model accuracy and interpretation. The findings open the door to new ways to predict the simplest solutions and the most powerful and accurate way to date of estimating the danger zone.

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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 the need for additional filters or bulky microlenses for color discrimination or photon collection. Furthermore, the photocurrent generated by the TP photodetector can be regulated by varying light pulse stimulations, enabling it to mimic the synaptic dynamics of the human brain. By integrating the functions of perception, processing, and memorization of visual images, its potential for facial recognition through simulation based on a 64 × 64 array of TP photodetectors under the artificial neural network‐based weight‐update expectation thresholding model is demonstrated. These findings mark a significant step forward in utilizing all‐inorganic perovskite materials for compact, color‐filter‐free image sensors and open new avenues for photonic neural computations on a single platform.

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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 inhibitory synaptic data. Compatibility with synaptic devices and thresholding operation is achieved using a single flux quantum (SFQ) pulse-based logic style. The neuron design, along with ternary synaptic connections, forms the foundation for a superconductor-based SNN inference. To demonstrate the capabilities of our design, we train the SNN using snnTorch, augmenting the PyTorch framework. After pruning, the demonstrated SNN inference achieves an impressive 96.1% accuracy on MNIST images. Notably, the network exhibits a remarkable throughput of 8.92 GHz while consuming only 1.5 nJ per inference, including the energy consumption associated with cooling to 4K. These results underscore the potential of superconductor electronics in developing high-performance and ultra-energy-efficient neural network accelerator architectures.

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