Update 2024.md

2024/2024.md

@@ -67,6 +67,7 @@
 - [Makarov et al., 2024, Quantum Optimization Methods for Satellite Mission Planning](https://arxiv.org/pdf/2404.05516)
 - [Minuto & Caletti & Solinas, 2024, A Novel Approach to Reduce Derivative Costs in Variational Quantum Algorithms]()
 - [Mirani & Hayden, 2024, Learning interacting fermionic Hamiltonians at the Heisenberg limit](https://arxiv.org/pdf/2403.00069)
+- [Molteni & Gyurik & Dunjko, 2024, Exponential quantum advantages in learning quantum observables from classical data](https://arxiv.org/pdf/2405.02027)
 - [Montalbano & Banchi, 2024, Quantum Adversarial Learning for Kernel Methods](https://arxiv.org/pdf/2404.05824)
 - [Montanez-Barrera & Willsch & Michielsen, 2024, Transfer learning of optimal QAOA parameters in combinatorial optimization](https://arxiv.org/pdf/2402.05549)
 - [Morris & Lotshaw, 2024, Performant near-term quantum combinatorial optimization](https://arxiv.org/pdf/2404.16135)
@@ -84,6 +85,7 @@
 - [Ruiz et al., 2024, Quantum Circuit Optimization with AlphaTensor](https://arxiv.org/pdf/2402.14396)
 - [Sadhu & Sarkar & Kundu, 2024, A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search](https://arxiv.org/pdf/2404.06174)
 - [Sahin et al., 2024, Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training](https://arxiv.org/pdf/2401.02879)
+- [Schillo & Sturm, 2024, Quantum Circuit Learning on NISQ Hardware](https://arxiv.org/pdf/2405.02069)
 - [Schnaus et al., 2024, Efficient Encodings of the Travelling Salesperson Problem for Variational Quantum Algorithms](https://arxiv.org/pdf/2404.05448)
 - [Scholten et al., 2024, Assessing the Benefits and Risks of Quantum Computers](https://browse.arxiv.org/pdf/2401.16317)
 - [Schumacher et al., 2024, Hybrid Tree Tensor Networks for quantum simulation](https://arxiv.org/pdf/2404.05784)