Update 2024.md

2024/2024.md

@@ -22,6 +22,7 @@
 - [Bittel et al., 2024, PAC-learning of free-fermionic states is NP-hard](https://arxiv.org/pdf/2404.03585)
 - [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)
 - [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)
@@ -108,6 +109,7 @@
 - [Oshio et al., 2024, Adaptive measurement strategy for noisy quantum amplitude estimation with variational quantum circuits](https://arxiv.org/pdf/2405.15174)
 - [Ott et al., 2024, Hamiltonian Learning in Quantum Field Theories](https://arxiv.org/pdf/2401.01308)
 - [Pal & Pal, 2024, Time-dependent Hamiltonians and Geometry of Operators Generated by Them](https://arxiv.org/pdf/2405.14410)
+- [Park et al., 2024, Global optimization in variational quantum algorithms via dynamic tunneling method](https://arxiv.org/pdf/2405.18783)
 - [Perlin et al., 2024, Q-CHOP: Quantum constrained Hamiltonian optimization](https://arxiv.org/pdf/2403.05653)
 - [Power & Guha, 2024, Feature Importance and Explainability in Quantum Machine Learning](https://arxiv.org/pdf/2405.08917)
 - [Razavinia & Haghighatdoost, 2024, A route to quantum computing through the theory of quantum graphs](https://arxiv.org/pdf/2404.13773)