Update 2025.md

2025/2025.md

@@ -8,6 +8,7 @@
 - [Arai & Kadowaki, 2025, Quantum Annealing Enhanced Markov-Chain Monte Carlo](https://arxiv.org/pdf/2502.08060)
 - [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)
 - [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)
@@ -55,6 +56,7 @@
 - [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)
+- [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)
 
 #### Inspired