From f65ebebf18f0c3900a5aae6a43ba614a89d80e25 Mon Sep 17 00:00:00 2001 From: Christophe Pere Date: Wed, 26 Feb 2025 15:34:17 -0500 Subject: [PATCH] Update 2025.md --- 2025/2025.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/2025/2025.md b/2025/2025.md index b5b616b..b6191f6 100644 --- a/2025/2025.md +++ b/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