01 / PUBLISHED RESEARCH
[←]Return to ServicesPeer-reviewed publications.
Authored papers from the Trinexis Quantum research team — spanning hybrid quantum neural networks, quantum NLP, healthcare 5.0 diagnostics, and edge AI — presented at IEEE conferences and indexed by IEEE, Taylor & Francis, and CRC Press.
Contextual and Spatial Attention Model: Transfer Learning based Hybrid QNN Paradigm for Computer Vision Applications
Quantum Machine Learning & Computer Vision
Transfer learning accuracy and inference benchmarks across pre-trained CV architectures integrated with hybrid end-to-end QNN circuits for real-world computer vision deployment on AI edge devices.
IEEE International Conference on Quantum Computing and Engineering (QCE)
Broomfield, CO, USA
A Hybrid QNN-Based Framework for Accurate Early Detection of HCV Liver Abnormalities from CT Scans Using Custom Transfer Learning and AI Edge Device
Healthcare & Quantum Edge AI
End-to-end hybrid QNN framework leveraging custom transfer learning to detect HCV-related liver lesions from CT scans, with hardware accelerators deployed on AI edge devices onboard CT scanners.
IEEE Region 10 Symposium (TENSYMP)
Canberra, ACT, Australia
Revolutionizing Staffing and Recruiting with Contextual Knowledge Graphs and QNLP: An End-to-End Quantum Training Paradigm
Quantum NLP & Knowledge Graphs
End-to-end compositional quantum natural language processing (QNLP) combined with contextual knowledge graphs for high-dimensional semantic search and talent matching.
IEEE International Conference on Knowledge Graph (ICKG)
Shanghai, China
Revolutionizing COVID-19 Diagnosis: A Hybrid Quantum Neural Network-based Framework for Accurate Detection of Lung Abnormalities from CT Scans
Healthcare & Edge Quantum AI
Edge-deployable hybrid quantum neural network framework achieving state-of-the-art diagnostic accuracy for pulmonary abnormalities on compact AI edge devices.
Edge AI for Industry 5.0 and Healthcare 5.0 Applications (CRC Press)
Boca Raton, FL, USA
A Novel Hybrid CNN–Quantum Neural Network Framework with Quantum Acceleration and Error Correction for High-Precision Breast Cancer Classification on AI Edge Devices
Healthcare & Quantum Edge AI
Hybrid ResNet152–QNN framework with Shor's Code and surface-code error correction achieving 97% breast cancer classification accuracy at 0.032 fps on AI edge devices.
2025 IEEE World AI IoT Congress (AIIoT)
Seattle, WA, USA
Quantum-Enhanced Hybrid CNN–QNN Framework for Intelligent Rice Disease Diagnosis and Real-Time Field Recommendations on Edge AI Devices
Precision Agriculture & Quantum Edge AI
Hybrid CNN–QNN model with parameterized quantum circuits and quantum error correction achieving 96.8% accuracy for real-time rice disease diagnosis and agronomic recommendations on edge devices.
2025 IEEE 7th Symposium on Computers & Informatics (ISCI)
Kuala Lumpur, Malaysia