Update 2025.md

2025/2025.md

@@ -9,13 +9,17 @@
 - [Asaoka & Kudo, 2025, Quantum autoencoders for image classification](https://arxiv.org/pdf/2502.15254)
 - [Babu et al., 2025, Gate teleportation-assisted routing for quantum algorithms](https://arxiv.org/pdf/2502.04138)
 - [Bal et al., 2025, 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning](https://arxiv.org/pdf/2502.17301)
+- [Berti et al., 2025, Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?](https://arxiv.org/pdf/2502.18639)
 - [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)
 - [Coelho & Kruse & Rosskopf, 2025, Quantum-Efficient Kernel Target Alignment](https://arxiv.org/pdf/2502.08225)
 - [Coroi & Oh, 2025, Exponential advantage in continuous-variable quantum state learning](https://arxiv.org/pdf/2501.17633)
+- [Dell'Anna et al., 2025, Quantum Natural Gradient optimizer on noisy platforms: QAOA as a case study](https://arxiv.org/pdf/2502.20288)
 - [Fleury & Lacomme, 2025, Quantum circuit for exponentiation of Hamiltonians: an algorithmic description based on tensor products](https://arxiv.org/pdf/2501.17780)
+- [Galvão et al., 2025, Simulating Work Extraction in a Dinuclear Quantum Battery Using a Variational Quantum Algorithm](https://arxiv.org/pdf/2502.19331)
 - [Galvis-Florez & Farooq & Särkkä, 2025, Provable Quantum Algorithm Advantage for Gaussian Process Quadrature](https://arxiv.org/pdf/2502.14467)
 - [Gerlach, et al. 2025, Hybrid Quantum-Classical Multi-Agent Pathfinding](https://arxiv.org/pdf/2501.14568)
+- [Hoch et al., 2025, Quantum machine learning with Adaptive Boson Sampling via post-selection](https://arxiv.org/pdf/2502.20305)
 - [Joch & Uhrig & Fauseweh, 2025, Entanglement-informed Construction of Variational Quantum Circuits](https://arxiv.org/pdf/2501.17533)
 - [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)
@@ -51,11 +55,13 @@
 - [Tomar & Tripathi & Kumar, 2025, Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements](https://arxiv.org/pdf/2501.09528)
 - [Vasques & Paik & Cif, 2025, Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification](https://arxiv.org/pdf/2502.06281)
 - [Villar-Rodriguez et al., 2025, On the Transfer of Knowledge in Quantum Algorithms](https://arxiv.org/pdf/2501.14120)
+- [Vyas & Santhanam, 2025, Extreme Events of Quantum Walks on Graphs](https://arxiv.org/pdf/2502.19355)
 - [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)
 - [Wang et al., 2025, Towards efficient quantum algorithms for diffusion probability models](https://arxiv.org/pdf/2502.14252)
 - [Xu & Aggarwal, 2025, Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach](https://arxiv.org/pdf/2501.16243)
+- [Zhang et al., 2025, Hamiltonian Learning at Heisenberg Limit for Hybrid Quantum Systems](https://arxiv.org/pdf/2502.20373)
 - [Zia et al., 2025, Quantum extreme learning machines for photonic entanglement witnessing](https://arxiv.org/pdf/2502.18361)
 - [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)