Update 2024.md

2024/2024.md

@@ -10,6 +10,7 @@
 - [Acuaviva et al., 2024, Benchmarking Quantum Computers: Towards a Standard Performance Evaluation Approach](https://arxiv.org/pdf/2407.10941)
 - [Ahmed & Tennie & Magri, 2024, Prediction of chaotic dynamics and extreme events: A recurrence-free quantum reservoir computing approach](https://arxiv.org/pdf/2405.03390)
 - [Ai & Liu, 2024, Graph Neural Networks-based Parameter Design towards Large-ScaleSuperconducting Quantum Circuits for Crosstalk Mitigation](https://arxiv.org/pdf/2411.16354)
+- [Akande & Senjean & Saubanere, 2024, Symmetry-preserved cost functions for variational quantum eigensolver](https://arxiv.org/pdf/2411.16915)
 - [Akpinar & Islam & Oduncuoglu, 2024, Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application](https://arxiv.org/pdf/2407.09930)
 - [Aktar et al., 2024, Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation](https://arxiv.org/pdf/2405.08100)
 - [Alexeev et al., 2024, Artificial Intelligence for Quantum Computing](https://arxiv.org/pdf/2411.09131)
@@ -38,6 +39,7 @@
 - [Blenninger et al., 2024, Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects](https://arxiv.org/pdf/2403.17495)
 - [Bluhm & Caro & Oufkir, 2024, Hamiltonian Property Testing](https://arxiv.org/pdf/2403.02968)
 - [Borle & Bhave, 2024, Biclustering a dataset using photonic quantum computing](https://arxiv.org/pdf/2405.18622)
+- [Boulebnane et al., 2024, Applying the quantum approximate optimization algorithm to general constraint satisfaction problems](https://arxiv.org/pdf/2411.17442)
 - [Bowles & Ahmed & Schuld, 2024, Better than classical? The subtle art of benchmarking quantum machine learning models](https://arxiv.org/pdf/2403.07059)
 - [Bucher et al., 2024, Towards Robust Benchmarking of Quantum Optimization Algorithms](https://arxiv.org/pdf/2405.07624)
 - [Calderón et al., 2024, Measurement-based quantum machine learning](https://arxiv.org/pdf/2405.08319)
@@ -133,8 +135,10 @@
 - [Labay Mora, et al., 2024, Theoretical framework for quantum associative memories](https://arxiv.org/pdf/2408.14272)
 - [Lai et al., 2024, Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks](https://arxiv.org/pdf/2410.12636)
 - [Larson & Menickelly & Shi, 2024, A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms](https://arxiv.org/pdf/2401.10121)
+- [Leclerc et al., 2024, Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles](https://arxiv.org/pdf/2411.16656)
 - [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)
+- [Levenson-Falk & Shanto, 2024, A Review of Design Concerns in Superconducting Quantum Circuits](https://arxiv.org/pdf/2411.16967)
 - [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)
@@ -193,6 +197,7 @@
 - [Perrier, 2024, Quantum Geometric Machine Learning](https://arxiv.org/pdf/2409.04955)
 - [Pirnay et al., 2024, An unconditional distribution learning advantage with shallow quantum circuits](https://arxiv.org/pdf/2411.15548)
 - [Power & Guha, 2024, Feature Importance and Explainability in Quantum Machine Learning](https://arxiv.org/pdf/2405.08917)
+- [Pranjic et al., 2024, Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors](https://arxiv.org/pdf/2411.16970)
 - [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)