Update 2024.md

2024/2024.md

@@ -100,6 +100,7 @@
 - [Magnusson et al., 2024, Towards Efficient Quantum Computing for Quantum Chemistry: Reducing Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques](https://arxiv.org/pdf/2402.16659)
 - [Makarov et al., 2024, Quantum Optimization Methods for Satellite Mission Planning](https://arxiv.org/pdf/2404.05516)
 - [Matwiejew & Wang, 2024, Quantum walk informed variational algorithm design](https://arxiv.org/pdf/2406.11620)
+- [Michel, 2024, Quantum simulation for strongly interacting fermions with neutral atoms array : towards the simulation of materials of interest](https://arxiv.org/pdf/2406.13343)
 - [Minuto & Caletti & Solinas, 2024, A Novel Approach to Reduce Derivative Costs in Variational Quantum Algorithms](https://arxiv.org/pdf/2404.02245)
 - [Mirani & Hayden, 2024, Learning interacting fermionic Hamiltonians at the Heisenberg limit](https://arxiv.org/pdf/2403.00069)
 - [Mironowicz et al., 2024, Applications of Quantum Machine Learning for Quantitative Finance](https://arxiv.org/pdf/2405.10119)
@@ -125,6 +126,7 @@
 - [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)
+- [Recio-Armengol & Eisert & Meyer, 2024, Single-shot quantum machine learning](https://arxiv.org/pdf/2406.13812)
 - [Rodriguez-Grasa & Ban & Sanz, 2024, https://arxiv.org/pdf/2401.04784](https://arxiv.org/pdf/2401.04642) 
 - [Romero & Milburn, 2024, Photonic Quantum Computing](https://arxiv.org/pdf/2404.03367)
 - [Rosenhahn & Hirche, 2024, Quantum Normalizing Flows for Anomaly Detection](https://arxiv.org/pdf/2402.02866)
@@ -177,10 +179,12 @@
 - [Wold & Ribeiro & Denisov, 2024, Universal spectra of noisy parameterized quantum circuits](https://arxiv.org/pdf/2405.11625)
 - [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)
+- [Yalovetzky et al., 2024, QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest](https://arxiv.org/pdf/2406.12008)
 - [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)
+- [Zalivako et al., 2024, Supervised binary classification of small-scale digits images with a trapped-ion quantum processor](https://arxiv.org/pdf/2406.12007)
 - [Zaman et al., 2024, A Comparative Analysis of Hybrid-Quantum Classical Neural Networks](https://arxiv.org/pdf/2402.10540)
 - [Zhang et al., 2024, A generalized cycle benchmarking algorithm for characterizing mid-circuit measurements](https://arxiv.org/pdf/2406.02669)
 - [Zhang, Zhu & Wang, 2024, Predicting quantum learnability from landscape fluctuation](https://arxiv.org/pdf/2406.11805)