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Author

Alexander Geng

Observed names: A. Geng, Geng, Alexander
ORCID: 0000-0002-9955-4809
Observed ORCID: 0000-0002-9955-4809
OpenAlex ID: A5082531414
A hybrid quantum image edge detector for the NISQ era
2022 · Alexander Geng, Ali Moghiseh, et al. · Quantum Machine Intelligence · 21
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Improved FRQI on superconducting processors and its restrictions in the NISQ era
2023 · Alexander Geng, Ali Moghiseh, et al. · Quantum Information Processing · 15
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LLM-generated tips rival expert-created tips in helping students answer quantum-computing questions
2025 · Lars Krupp, Jonas Bley, et al. · EPJ Quantum Technology · 10
Comparing optimization methods for deep learning in image processing applications
2021 · Alexander Geng, Ali Moghiseh, et al. · tm - Technisches Messen · 9
Quantum image processing on real superconducting and trapped-ion based quantum computers
2023 · Alexander Geng, Ali Moghiseh, et al. · tm - Technisches Messen · 7
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Hybrid quantum transfer learning for crack image classification on NISQ hardware
2025 · Alexander Geng, Ali Moghiseh, et al. · AIP conference proceedings · 6
A comparative study of Q-Seg, quantum-inspired techniques, and U-Net for crack image segmentation
2025 · Akshaya Srinivasan, Alexander Geng, et al. · tm - Technisches Messen · 3
Hybrid Quantum–Classical AI for Industrial Defect Classification in Welding Images
2026 · Akshaya Srinivasan, Xiaoyin Cheng, et al. · International Scientific Conference TechSys — Engineering, Technology and Systems (TechSys) · 2
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Hybrid quantum transfer learning for crack image classification on NISQ hardware
2023 · Geng, Alexander, Moghiseh, Ali, et al. · arXiv (Cornell University) · 1
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Improved FRQI on superconducting processors and its restrictions in the NISQ era
2021 · Geng, Alexander, Moghiseh, Ali, et al. · arXiv (Cornell University) · 1
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A Comprehensive Survey of Quantum and Quantum-Inspired Image Segmentation in the Context of Modern Segmentation Methods
2026 · Marie Gogolin, Yannick Werner, et al. · Research Square
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A Quantum Fourier Transform Approach for Image Alignment
2026 · Alexander Geng, Ali Moghiseh · International Scientific Conference TechSys — Engineering, Technology and Systems (TechSys)
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Quantum Image Encoding Fidelity Metric (QIEF) for Performance Evaluation of Quantum Devices
2026 · Alexander Geng, Ali Moghiseh · International Scientific Conference TechSys — Engineering, Technology and Systems (TechSys)
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Schmidt Decomposition-Based Methods for Efficient Quantum Image Encoding
2026 · Pangeva, Ana-Maria, Ferhi, Yassine, et al. · arXiv (Cornell University)
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Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images
2026 · Srinivasan, Akshaya, Cheng, Xiaoyin, et al. · arXiv (Cornell University)
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Pattern Matching Using Sliding Windows on Quantum Computers
2026 · Y. Ferhi, A. Geng, et al. · Communications in computer and information science
EniQmA - Enabling Hybrid Quantum Applications
2026 · Dimitris Badounas, Marvin Bechtold, et al. · Communications in computer and information science
Quant$$^{\textbf {2}}$$AI – An End-to-End Quantum AI Benchmarking Framework for Both Researchers and Practitioners
2026 · Cristobal Corvalan Morbiducci, Pascal Halffmann, et al. · Communications in computer and information science
Hybrid Quantum–Classical AI for Industrial Defect Classification in Welding Images †
2026 · Srinivasan, Akshaya, Cheng, Xiaoyin, et al. · Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation
2024 · Srinivasan, Akshaya, Geng, Alexander, et al. · arXiv (Cornell University)
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Benefiting from quantum? A comparative study of Q-Seg, quantum-inspired techniques, and U-Net for crack segmentation
2024 · Akshaya Srinivasan, Alexander Geng, et al. · Forum Bildverarbeitung (Image Processing Forum)
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In diesem Beitrag wird die semantische Segmentierung von Ankern aus Elektromotoren und seinen Komponenten untersucht. Hierf ¨ ur wird ein U-Net mit einem eigenst¨andig angefertigten Datensatz trainiert, welcher aus Bildern von Ankern unterschiedlichster Bauformen besteht und im Rahmen dieses Beitrags angefertigt wurde. Aufgrund der geringen Anzahl von 75 Trainingsbildern werden neben einer geeigneten Standardaugmentierung auch eine neuartige Hintergrundaugmentierung und das Einbinden von Kanteninformationen untersucht. Mithilfe dieser Methoden kann der Testfehler bei der Segmentierung um insgesamt 70% reduziert werden.
2020 · Alexander Geng, Ali Moghiseh, et al. · Forum Bildverarbeitung (Image Processing Forum)
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Optimization Algorithm for Single-Isocenter VMAT Stereotactic Radiosurgery for Multiple Brain Metastases
2016 · Gordon Wells, J Ho, et al. · Cureus
Works count: 28
Citation count: 76
H-index: 6
I10-index: 3
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