Update 2024.md

2024/2024.md

@@ -95,6 +95,7 @@
 - [Lee & Park, 2024, Quadratic speed-ups in quantum kernelized binary classification](https://arxiv.org/pdf/2403.17453)
 - [Li et al., 2024, Quantum molecular docking with quantum-inspired algorithm](https://arxiv.org/pdf/2404.08265)
 - [Li, 2024, Variational methods for solving high dimensional quantum systems](https://arxiv.org/pdf/2404.11490)
+- [Li & Tong, 2024, Exponential Quantum Advantage for Pathfinding in Regular Sunflower Graphs](https://arxiv.org/pdf/2407.14398)
 - [Liang et al., 2024, Learnability of a hybrid quantum-classical neural network for graph-structured quantum data](https://browse.arxiv.org/pdf/2401.15665)
 - [Liao & Zhang & Ferrie, 2024, Graph Neural Networks on Quantum Computers](https://arxiv.org/pdf/2405.17060)
 - [Liu et al., 2024, Quantum accelerated cross regression algorithm for multiview feature extraction](https://arxiv.org/pdf/2403.17444)
@@ -109,7 +110,9 @@
 - [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)
+- [Miransky & Khan & Mendes, 2024, Comparing Algorithms for Loading Classical Datasets into Quantum Memory](https://arxiv.org/pdf/2407.15745)
 - [Mironowicz et al., 2024, Applications of Quantum Machine Learning for Quantitative Finance](https://arxiv.org/pdf/2405.10119)
+- [Miroszewski et al., 2024, In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods](https://arxiv.org/pdf/2407.15776)
 - [Mohseni et al., 2024, A Competitive Showcase of Quantum versus Classical Algorithms in Energy Coalition Formation](https://arxiv.org/pdf/2405.11917)
 - [Molteni & Gyurik & Dunjko, 2024, Exponential quantum advantages in learning quantum observables from classical data](https://arxiv.org/pdf/2405.02027)
 - [Montalbano & Banchi, 2024, Quantum Adversarial Learning for Kernel Methods](https://arxiv.org/pdf/2404.05824)
@@ -178,6 +181,7 @@
 - [Wang & Wang, 2024, Time series prediction of open quantum system dynamics](https://arxiv.org/pdf/2401.06380)
 - [Wang et al., 2024, Power Characterization of Noisy Quantum Kernels](https://browse.arxiv.org/pdf/2401.17526)
 - [Wang et al., 2024, Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance](https://arxiv.org/pdf/2403.04296)
+- [Wang et al., 2024, Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers](https://arxiv.org/pdf/2407.14055)
 - [Waring & Pere & Le Beux, 2024, XGSwap: eXtreme Gradient boosting Swap for Routing in NISQ Devices](https://arxiv.org/pdf/2404.17982)
 - [Watanabe & Ueda, 2024, Automatic Structural Search of Tensor Network States including Entanglement Renormalization](https://arxiv.org/pdf/2405.06534)
 - [Wiersema et al., 2024, Geometric Quantum Machine Learning with Horizontal Quantum Gates](https://arxiv.org/pdf/2406.04418)