diff --git a/2025/2025.md b/2025/2025.md
index 174e56c..e1fddcc 100644
--- a/2025/2025.md
+++ b/2025/2025.md
@@ -19,8 +19,7 @@
 - [Khesin, 2025, Quantum Computing from Graphs](https://arxiv.org/pdf/2501.17959)
 - [Kemples et al., 2025, Double descent in quantum machine learning](https://arxiv.org/pdf/2501.10077)
 - [Lall et al., 2025, A Review and Collection of Metrics and Benchmarks for Quantum Computers: definitions, methodologies and software](https://arxiv.org/pdf/2502.06717)
-- [Lee & Cho & Kim, 2025, Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum
-Algorithms](https://arxiv.org/pdf/2501.05906)
+- [Lee & Cho & Kim, 2025, Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms](https://arxiv.org/pdf/2501.05906)
 - [Li, et al., 2025, Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction](https://arxiv.org/pdf/2501.04264)
 - [Lipardi et al., 2025, Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search](https://arxiv.org/pdf/2502.03962)
 - [Liu et al., 2025, Quantum learning advantage on a scalable photonic platform](https://arxiv.org/pdf/2502.07770)
@@ -33,6 +32,7 @@ Algorithms](https://arxiv.org/pdf/2501.05906)
 - [Nakada & Tanahashi & Tanaka, 2025, Inductive Construction of Variational Quantum Circuit for Constrained Combinatorial Optimization](https://arxiv.org/pdf/2501.03521)
 - [Nghiem, 2025, New Quantum Algorithm for Principal Component Analysis](https://arxiv.org/pdf/2501.07891)
 - [Osaba et al., 2025, Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms](https://arxiv.org/pdf/2501.18432)
+- [Pastori et al., 2025, Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models](https://arxiv.org/pdf/2502.10131)
 - [Patel et al., 2025, Quantum Measurement for Quantum Chemistry on a Quantum Computer](https://arxiv.org/pdf/2501.14968)
 - [Rosaler et al., 2025, Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning](https://arxiv.org/pdf/2502.01495)
 - [Schetakis et al., 2025, Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting](https://arxiv.org/pdf/2501.12776)