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ITWM FM QC Public / cvqa
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This is the code developed by Dr.-Ing. Dominique Koster and contributed to by Dr. Mariam R. Rizkallah for the workshop "Data Reconciliation: Integration of Electrochemical Data and Optical Sensor Data for Health Diagnostics of Lithium-Ion Batteries" https://www.bremen-research.de/data-train/courses/course-details?event_id=88
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Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights) during the aggregation step. A significant challenge in FL is managing the feature distribution of novel, unbalanced data across devices. In this paper, we propose an FL approach using few-shot learning and aggregation of the model weights on a global server. We introduce a dynamic early stopping method to balance out-of-distribution classes based on representation learning, specifically utilizing the maximum mean discrepancy of feature embeddings between local and global models. An exemplary application of FL is orchestrating machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers. Extensive experiments on four GNSS datasets from two real-world highways and controlled environments demonstrate that our FL method surpasses state-of-the-art techniques in adapting to both novel interference classes and multipath scenarios.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Sensitivity Analysis for Network Failure
Apache License 2.0Here we perform a hybrid, Grover based optimization to find the single network parameter change that leads to the largest reduction of the critical failure probability in a cascading network
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Fraunhofer IAO QC / SEQUOIA End-to-End / Error Mitigation by Zero Noise Extrapolation
Apache License 2.0Demonstrator for Zero-Noise Extrapolation (ZNE) and Inverted-Circuit ZNE
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Fraunhofer IAO QC / SEQUOIA End-to-End / Quantum-based Computational Fluid Dynamics with Quantum Circuit Learning
Apache License 2.0A powerful example of variational quantum algorithms is the so-called quantum circuit learning algorithm (QCL), which approximates functions and can solve non-linear differential equations by using the parameter shift rule. This demonstrator aims to explain the basics of QCL and uses examples to show how different functions can be approximated and differential equations can be solved.
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