Merge branch 'main' of https://github.com/Christophe-pere/Roadmap-to-QML

Christophe-pere · Nov 22, 2024 · b5143ac · b5143ac
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diff --git a/2024/2024.md b/2024/2024.md
@@ -41,7 +41,7 @@
 - [Calderón et al., 2024, Measurement-based quantum machine learning](https://arxiv.org/pdf/2405.08319)
 - [Canonici & Caruso, 2024, QUBO-based SVM for credit card fraud detection on a real QPU](https://arxiv.org/pdf/2409.11876)
 - [Cantori & Pilati, 2024, Challenges and opportunities in the supervised learning of quantum circuit outputs](https://arxiv.org/pdf/2402.04992)
-- [Cara et al., 2024, Quantum Vision Transformers for Quark-Gluon Classification](https://arxiv.org/pdf/2405.10284)
+- [Comajoan Cara et al., 2024, Quantum Vision Transformers for Quark-Gluon Classification](https://arxiv.org/pdf/2405.10284)
 - [Carugno & Cadrema & Cremonesi, 2024, Adaptive Learning for Quantum Linear Regression](https://arxiv.org/pdf/2408.02833)
 - [Cemin et al., 2024, Machine learning of quantum channels on NISQ devices](https://arxiv.org/pdf/2405.12598)
 - [Chakraborty et al., 2024, A Machine Learning Approach for Optimizing Hybrid Quantum Noise Clusters for Gaussian Quantum Channel Capacity](https://arxiv.org/pdf/2404.08993)
@@ -279,4 +279,4 @@
 #### Inspired 
 
 - [Du & Liu & Guo, 2024, A quantum inspired neural network for geometric modeling](https://arxiv.org/pdf/2401.01801)
-- [Shu et al., 2024, Quantum-Inspired Machine Learning for Molecular Docking](https://arxiv.org/pdf/2401.12999)
+- [Shu et al., 2024, Quantum-Inspired Machine Learning for Molecular Docking](https://arxiv.org/pdf/2401.12999)