diff --git a/2024/2024.md b/2024/2024.md
index 9b77703..cea51aa 100644
--- a/2024/2024.md
+++ b/2024/2024.md
@@ -9,6 +9,9 @@
 - [Aktar et al., 2024, Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation](https://arxiv.org/pdf/2405.08100)
 - [Ali & Kabel, 2024, Piecewise Polynomial Tensor Network Quantum Feature Encoding](https://arxiv.org/pdf/2402.07671)
 - [Aminpour & Sharif, 2024, Strategic Data Re-Uploads: A Pathway to Improved Quantum Classification Data Re-Uploading Strategies for Improved Quantum Classifier Performance](https://arxiv.org/pdf/2405.09377)
+- [An et al., 2024, Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction](https://arxiv.org/pdf/2405.13582)
+- [Arjona-Medina & Nugmanov, 2024, Analysis of Atom-level pretraining with QM data for Graph Neural Networks Molecular property models](https://arxiv.org/pdf/2405.14837)
+- [Bakó et al., 2024, Problem-informed Graphical Quantum Generative Learning](https://arxiv.org/pdf/2405.14072)
 - [Bangar et al., 2024, Continuous-variable Quantum Boltzmann Machine](https://arxiv.org/pdf/2405.06580)
 - [Barthe et al., 2024, Expressivity of parameterized quantum circuits for generative modeling of continuous multivariate distributions](https://arxiv.org/pdf/2402.09848)
 - [Battaglia et al., 2024, A general framework for active space embedding methods: applications in quantum computing](https://arxiv.org/pdf/2404.18737)
@@ -24,6 +27,7 @@
 - [Calderón et al., 2024, Measurement-based quantum machine learning](https://arxiv.org/pdf/2405.08319)
 - [Cantori & Pilati, 2024, Challenges and opportunities in the supervised learning of quantum circuit outputs](https://arxiv.org/pdf/2402.04992)
 - [Cara et al., 2024, Quantum Vision Transformers for Quark-Gluon Classification](https://arxiv.org/pdf/2405.10284)
+- [Cemin et al., 2024, Machine learning of quantum channels on NISQ devices](https://arxiv.org/pdf/2405.12598)
 - [Chakraborty et al., 2024, A Machine Learning Approach for Optimizing Hybrid Quantum Noise Clusters for Gaussian Quantum Channel Capacity](https://arxiv.org/pdf/2404.08993)
 - [Chen et al., 2024, Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study](https://arxiv.org/pdf/2405.11687)
 - [Chia & Liang & Song, 2024, Quantum State Learning Implies Circuit Lower Bounds](https://arxiv.org/pdf/2405.10242)
@@ -46,8 +50,10 @@
 - [Georgiou & Jose & Simeone, 2024, Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis](https://browse.arxiv.org/pdf/2402.00176)
 - [Gerlach & Mücke, 2024, Investigating the Relation Between Problem Hardness and QUBO Properties](https://arxiv.org/pdf/2404.02751)
 - [Goldschmith & Mahmud, 2024, Machine Learning for Quantum Computing Specialists](https://arxiv.org/pdf/2404.18555)
+- [Gonzales et al., 2024, Detecting Errors in a Quantum Network with Pauli Checks](https://arxiv.org/pdf/2405.15236)
 - [Gustafson et al., 2024, Surrogate optimization of variational quantum circuits](https://arxiv.org/pdf/2404.02951)
 - [He, 2024, Quantum Annealing and Graph Neural Networks for Solving TSP with QUB0](https://arxiv.org/pdf/2402.14036)
+- [Hegde et al., 2024, Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications](https://arxiv.org/pdf/2405.14561)
 - [Herbst & De Maio & Brandi, 2024, On Optimizing Hyperparameters for Quantum Neural Networks](https://arxiv.org/pdf/2403.18579)
 - [Higushi & Segawa, 2024, Quantum walks on graphs embedded in orientable surfaces](https://browse.arxiv.org/pdf/2402.00360)
 - [Hryniuk & Szymańska, 2024, Tensor-network-based variational Monte Carlo approach to the non-equilibrium steady state of open quantum systems](https://arxiv.org/pdf/2405.12044)
@@ -65,6 +71,7 @@
 - [King, et al., 2024, Computational supremacy in quantum simulation](https://arxiv.org/pdf/2403.00910.pdf)
 - [Kiwit et al., 2024, Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK](https://arxiv.org/pdf/2403.18662)
 - [Krovi, 2024, Quantum algorithms to simulate quadratic classical Hamiltonians and optimal control](https://arxiv.org/pdf/2404.07303.pdf)
+- [Kukliansky et al., 2024, Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation](https://arxiv.org/pdf/2405.12866)
 - [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)
 - [Larson & Menickelly & Shi, 2024, A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms](https://arxiv.org/pdf/2401.10121)
@@ -94,7 +101,9 @@
 - [de Oliveira et al., 2024, Demonstration of weighted graph optimization on a Rydberg atom array using local light-shifts](https://arxiv.org/pdf/2404.02658)
 - [Okumura & Ohzeki & Abe, 2024, Application of time-series quantum generative model to financial data](https://arxiv.org/pdf/2405.11795)
 - [Olsacher et al., 2024, Hamiltonian and Liouvillian learning in weakly-dissipative quantum many-body systems](https://arxiv.org/pdf/2405.06768)
+- [Oshio et al., 2024, Adaptive measurement strategy for noisy quantum amplitude estimation with variational quantum circuits](https://arxiv.org/pdf/2405.15174)
 - [Ott et al., 2024, Hamiltonian Learning in Quantum Field Theories](https://arxiv.org/pdf/2401.01308)
+- [Pal & Pal, 2024, Time-dependent Hamiltonians and Geometry of Operators Generated by Them](https://arxiv.org/pdf/2405.14410)
 - [Perlin et al., 2024, Q-CHOP: Quantum constrained Hamiltonian optimization](https://arxiv.org/pdf/2403.05653)
 - [Power & Guha, 2024, Feature Importance and Explainability in Quantum Machine Learning](https://arxiv.org/pdf/2405.08917)
 - [Razavinia & Haghighatdoost, 2024, A route to quantum computing through the theory of quantum graphs](https://arxiv.org/pdf/2404.13773)
@@ -105,7 +114,9 @@
 - [Ruiz et al., 2024, Quantum Circuit Optimization with AlphaTensor](https://arxiv.org/pdf/2402.14396)
 - [Sadhu & Sarkar & Kundu, 2024, A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search](https://arxiv.org/pdf/2404.06174)
 - [Sahin et al., 2024, Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training](https://arxiv.org/pdf/2401.02879)
+- [Sakurai et al., 2024, Simple Hamiltonian dynamics is a powerful quantum processing resource](https://arxiv.org/pdf/2405.14245)
 - [Sanavio et al., 2024, Quantum Circuit for Imputation of Missing Data](https://arxiv.org/pdf/2405.04367)
+- [Sarkar, Chakraborty, Adhikari, 2024, Quantum circuit model for Hamiltonian simulation via Trotter decomposition](https://arxiv.org/pdf/2405.13605)
 - [Schillo & Sturm, 2024, Quantum Circuit Learning on NISQ Hardware](https://arxiv.org/pdf/2405.02069)
 - [Schnaus et al., 2024, Efficient Encodings of the Travelling Salesperson Problem for Variational Quantum Algorithms](https://arxiv.org/pdf/2404.05448)
 - [Scholten et al., 2024, Assessing the Benefits and Risks of Quantum Computers](https://browse.arxiv.org/pdf/2401.16317)
@@ -121,15 +132,18 @@
 - [Situ et al., 2024, Distributed quantum architecture search](https://arxiv.org/pdf/2403.06214)
 - [Smith & Sukhtayev, 2024 Splitting Quantum Graphs](https://arxiv.org/pdf/2402.07409)
 - [Song, 2024, Quantum walk on simplicial complexes for simplicial community detection](https://arxiv.org/pdf/2401.00699)
+- [Souza & Oliveira, 2024, Layers of planar hexagonal heterostructure modeled by quantum graphs](https://arxiv.org/pdf/2405.15029)
 - [Stechly, 2024, Introduction to Variational Quantum Algorithms](https://arxiv.org/pdf/2402.15879)
 - [Sturm & Mummaneni & Rullkötter, 2024, Unlocking Quantum Optimization: A Use Case Study on NISQ Systems](https://arxiv.org/pdf/2404.07171)
 - [Sun et al., 2024, Quantum Architecture Search with Unsupervised Representation Learning](https://arxiv.org/pdf/2401.11576)
 - [Suppakitpaisarn & Hao, 2024, Utilizing Graph Sparsification for Pre-processing in Maxcut QUBO Solver](https://arxiv.org/pdf/2401.13004)
 - [Suzuki & Sakuma & Kawaguchi, 2024, Light-cone feature selection for quantum machine learning](https://arxiv.org/pdf/2403.18733)
 - [Tucker et al., 2024, Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements](https://arxiv.org/pdf/2404.05526)
+- [Tucker et al., 2024, Quantum-assisted Rendezvous on Graphs: Explicit Algorithms and Quantum Computer Simulations](https://arxiv.org/pdf/2405.14951)
 - [Umeano & Elfving & Kyriienko, 2024, Geometric quantum machine learning of BQPA protocols and latent graph classifiers](https://arxiv.org/pdf/2402.03871)
 - [Umeano & Kyriienko, 2024, Ground state-based quantum feature maps](https://arxiv.org/pdf/2404.07174)
 - [van der Poel & Zhao, 2024, Entangling Machine Learning with Quantum Tensor Networks](https://arxiv.org/pdf/2403.12969)
+- [Venkatesh et al., 2024, Qubit-efficient Variational Quantum Algorithms for Image Segmentation](https://arxiv.org/pdf/2405.14405)
 - [Vinkhuijzen & Coopmans & Laarman, 2024, A Knowledge Compilation Map for Quantum Information](https://arxiv.org/pdf/2401.01322)
 - [Wang & Liu, 2024, Quantum Machine Learning: from NISQ to Fault Tolerance](https://arxiv.org/pdf/2401.11351)
 - [Wang & Wang, 2024, Time series prediction of open quantum system dynamics](https://arxiv.org/pdf/2401.06380)
@@ -142,6 +156,7 @@
 - [Wolf, 2024, Why we care (about quantum machine learning)](https://arxiv.org/pdf/2401.07547)
 - [Wu et al., 2024, Python-Based Quantum Chemistry Calculations with GPU Acceleration](https://arxiv.org/pdf/2404.09452)
 - [Yamamoto & Yoshioka, 2024, Robust Angle Finding for Generalized Quantum Signal Processing](https://arxiv.org/pdf/2402.03016)
+- [Yang et al., 2024, Quantum Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning](https://arxiv.org/pdf/2405.12625)
 - [Ye, 2024, QAOA on Hamiltonian Cycle problem](https://arxiv.org/pdf/2401.00017)
 - [Yogendran et al., 2024, Big data applications on small quantum computers](https://arxiv.org/pdf/2402.01529)
 - [Zaman et al., 2024, A Comparative Analysis of Hybrid-Quantum Classical Neural Networks](https://arxiv.org/pdf/2402.10540)