From b4aff419986a9486d2643f9482628f31a9da0a88 Mon Sep 17 00:00:00 2001 From: Christophe Pere Date: Tue, 16 Jul 2024 09:29:59 -0400 Subject: [PATCH] Update 2024.md --- 2024/2024.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/2024/2024.md b/2024/2024.md index fc84daf..ee5ed7a 100644 --- a/2024/2024.md +++ b/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)