Update 2024.md

2024/2024.md

@@ -23,6 +23,7 @@
 - [Baueur et al., 2024, Solving Power Grid Optimization Problems with Rydberg Atoms](https://arxiv.org/pdf/2404.11440)
 - [Beaudoin & Palak & Ghosh, 2024, AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization](https://arxiv.org/pdf/2403.12979)
 - [Bennett & Noakes & Wang, 2024, Non-variational Quantum Combinatorial Optimisation](https://arxiv.org/pdf/2404.03167)
+- [Bermejo et al., 2024, Quantum Convolutional Neural Networks are (Effectively) Classically Simulable](https://arxiv.org/pdf/2408.12739)
 - [Bilek, 2024, Quantum Machine Learning With a Limited Number Of Qubits](https://arxiv.org/pdf/2403.14406)
 - [Bittel et al., 2024, PAC-learning of free-fermionic states is NP-hard](https://arxiv.org/pdf/2404.03585)
 - [Blenninger et al., 2024, Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects](https://arxiv.org/pdf/2403.17495)
@@ -48,6 +49,7 @@
 - [Dalyak metal., 2024, Graph Algorithms with Neutral Atom Quantum Processors](https://arxiv.org/pdf/2403.11931)
 - [Daskin, 2024, A unifying primary framework for quantum graph neural networks from quantum graph states](https://arxiv.org/pdf/2402.13001)
 - [Date et al., 2024, Adiabatic Quantum Support Vector Machines](https://arxiv.org/pdf/2401.12485)
+- [De Luca, et al., 2024, Empirical Power of Quantum Encoding Methods for Binary Classification](https://arxiv.org/pdf/2408.13109)
 - [DeRieux & Saad, 2024, eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels](https://arxiv.org/pdf/2405.17486)
 - [Ding et al., 2024, Quantum Advantage: A Single Qubit's Experimental Edge in Classical Data Storage](https://arxiv.org/pdf/2403.02659)
 - [Ding et al., 2024, Quantum Active Learning](https://arxiv.org/pdf/2405.18230)
@@ -101,6 +103,7 @@
 - [Kundu, 2024, Reinforcement learning-assisted quantum architecture search for variational quantum algorithms](https://arxiv.org/pdf/2402.13754)
 - [Kundu et al., 2024, Application of Quantum Tensor Networks for Protein Classification](https://arxiv.org/pdf/2403.06890)
 - [Kundu & Ghosh, 2024, Security Concerns in Quantum Machine Learning as a Service](https://arxiv.org/pdf/2408.09562)
+- [Labay Mora, et al., 2024, Theoretical framework for quantum associative memories](https://arxiv.org/pdf/2408.14272)
 - [Larson & Menickelly & Shi, 2024, A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms](https://arxiv.org/pdf/2401.10121)
 - [Lee et al., 2024, Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension](https://arxiv.org/pdf/2403.19099)
 - [Lee & Park, 2024, Quadratic speed-ups in quantum kernelized binary classification](https://arxiv.org/pdf/2403.17453)