Update 2024.md

2024/2024.md

@@ -5,7 +5,9 @@
 #### 2024
 
 - [Abbas & Maksymo, 2024, Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://arxiv.org/pdf/2403.01024)
+- [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)
+- [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)
 - [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)
@@ -55,10 +57,13 @@
 - [Gacon, 2024, Scalable Quantum Algorithms for Noisy Quantum Computers](https://arxiv.org/pdf/2403.00940)
 - [Garrigues & Onofre & Bosc-Haddad, 2024 Towards molecular docking with neutral atoms](https://arxiv.org/pdf/2402.06770)
 - [Georgiou & Jose & Simeone, 2024, Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis](https://browse.arxiv.org/pdf/2402.00176)
+- [Gerblich et al., 2024, Advantages of multistage quantum walks over QAOA](https://arxiv.org/pdf/2407.06663)
 - [Gerlach & Mücke, 2024, Investigating the Relation Between Problem Hardness and QUBO Properties](https://arxiv.org/pdf/2404.02751)
 - [Gil-Fuster et al., 2024, On the relation between trainability and dequantization of variational quantum learning models](https://arxiv.org/pdf/2406.07072)
 - [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)
+- [Gosh & Gosh, 2024, The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models](https://arxiv.org/pdf/2407.07237)
+- [Gratsea et al., 2024, OnionVQE Optimization Strategy for Ground State Preparation on NISQ Devices](https://arxiv.org/pdf/2407.10415)
 - [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)
@@ -95,6 +100,7 @@
 - [Liu et al., 2024, Quantum accelerated cross regression algorithm for multiview feature extraction](https://arxiv.org/pdf/2403.17444)
 - [Liu et al., 2024, Quantum algorithms for matrix geometric means](https://arxiv.org/pdf/2405.00673)
 - [Liu et al., 2024, First Tree-like Quantum Data Structure: Quantum B+ Tree](https://arxiv.org/pdf/2405.20416)
+- [Liu et al., 2024, QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train](https://arxiv.org/pdf/2407.06103)
 - [Lu et al., 2024, Digital-analog quantum learning on Rydberg atom arrays](https://arxiv.org/pdf/2401.02940)
 - [Lubinski et al., 2024, Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks](https://arxiv.org/pdf/2402.08985)
 - [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)