Update 2025.md

2025/2025.md

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 - [Ahmed et al., 2025, Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation](https://arxiv.org/pdf/2501.14412)
 - [Chen et al., 2025, Learning to Measure Quantum Neural Networks](https://arxiv.org/pdf/2501.05663)
+- [Chen et al., 2025, Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions](https://arxiv.org/pdf/2501.15828)
 - [Gerlach, et al. 2025, Hybrid Quantum-Classical Multi-Agent Pathfinding](https://arxiv.org/pdf/2501.14568)
 - [Kairon & Jäger & Krems, 2025, Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms](https://arxiv.org/pdf/2501.07433)
 - [Kasture et al., 2025, Multiparticle quantum walks for distinguishing hard graphs](https://arxiv.org/pdf/2501.03683)
 - [Kemples et al., 2025, Double descent in quantum machine learning](https://arxiv.org/pdf/2501.10077)
 - [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)
+- [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)
 - [Nadim et al., 2025, Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://arxiv.org/pdf/2501.04690)
 - [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)
+- [Patel et al., 2025, Quantum Measurement for Quantum Chemistry on a Quantum Computer](https://arxiv.org/pdf/2501.14968)
 - [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)
+- [Tibaldi et al., 2025, Analog QAOA with Bayesian Optimisation on a neutral atom QPU](https://arxiv.org/pdf/2501.16229)
 - [Tomar & Tripathi & Kumar, 2025, Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements](https://arxiv.org/pdf/2501.09528)
 - [Villar-Rodriguez et al., 2025, On the Transfer of Knowledge in Quantum Algorithms](https://arxiv.org/pdf/2501.14120)
 - [Wang et al., 2025, GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching](https://arxiv.org/abs/2501.00135v1)
 - [Wang, 2025, QGHNN: A quantum graph Hamiltonian neural network](https://arxiv.org/pdf/2501.07986)
 - [Wang, 2025, Noise-resistant adaptive Hamiltonian learning](https://arxiv.org/pdf/2501.08017)
+- [Xu & Aggarwal, 2025, Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach](https://arxiv.org/pdf/2501.16243)
 - [Zimboràs et al., 2025, Myths around quantum computation before full fault tolerance: What no-go theorems rule out and what they don’t](https://arxiv.org/pdf/2501.05694)
 
 #### Inspired