Update 2025.md

2025/2025.md

@@ -32,6 +32,7 @@
 - [Liu et al., 2025, Quantum learning advantage on a scalable photonic platform](https://arxiv.org/pdf/2502.07770)
 - [Lo & Hsu & Kuo, 2025, Unsupervised Feature Extraction and Reconstruction Using Parameterized Quantum Circuits](https://arxiv.org/pdf/2502.07667)
 - [Mandal et al., 2025, Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review](https://arxiv.org/pdf/2502.08925)
+- [El Maouak et al., 2025, QFAL: Quantum Federated Adversarial Learning](https://arxiv.org/pdf/2502.21171)
 - [Meyer et al., 2025, Benchmarking Quantum Reinforcement Learning](https://arxiv.org/pdf/2501.15893)
 - [Minervini & Patel & Wilde, 2025, Evolved Quantum Boltzmann Machines](https://arxiv.org/pdf/2501.03367)
 - [Montanez-Barrera & Michielsen & Neira, 2025, Evaluating the performance of quantum process units at large width and depth](https://arxiv.org/pdf/2502.06471)
@@ -45,6 +46,7 @@
 - [Paula Neto, 2025, Data Complexity Measures for Quantum Circuits Architecture Recommendation](https://arxiv.org/pdf/2502.15129)
 - [Rosaler et al., 2025, Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning](https://arxiv.org/pdf/2502.01495)
 - [Rosenhahn & Osborne & Hirche, 2025, Optimization Driven Quantum Circuit Reduction](https://arxiv.org/pdf/2502.14715)
+- [Roth et al., 2025, AutoQML: A Framework for Automated Quantum Machine Learning](https://arxiv.org/pdf/2502.21025)
 - [Schetakis et al., 2025, Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting](https://arxiv.org/pdf/2501.12776)
 - [Singh & Pokhrel, 2025, Modeling Quantum Machine Learning for Genomic Data Analysis](https://arxiv.org/pdf/2501.08193)
 - [Singh & Pokhrel, 2025, Modeling Feature Maps for Quantum Machine Learning](https://arxiv.org/pdf/2501.08205)