Metasurfaces are innovative planar optical structures capable of manipulating incident light properties. Accurate and computationally efficient modeling of such metasurfaces, particularly those with irregular geometries, remains a challenge for conventional solvers. In this work, we present a mesh-free Physics-Informed PointNet (PIPN) to model electromagnetic scattering from all-dielectric metasurfaces that feature spatially varying nanopillars. Our approach uses the PointNet architecture to directly encode spatially varying material properties into the Physics-Informed Machine Learning (PIML) framework. We demonstrate the generalization capability of our PIPN through evaluations on datasets; these datasets are generated with varying refractive indices representing common dielectric materials. Furthermore, the inclination angles are varied within each dataset, which represent expected manufacturing defects. Overall, our method provides a promising, mesh-free framework for accurate and efficient modeling of complex optical structures represented by irregular geometries.

A distributed quantum network would require quantum nodes capable of performing arbitrary quantum information protocols with high fidelity. So far the challenge has been in realizing such quantum nodes with features for scalable quantum computing. We show here that using the solid-state spins in 4H-Silicon Carbide (4H-SiC) such a goal could be realized, wherein a controlled generation of highly coherent qubit registers using nuclear spins is possible. Using a controlled isotope concentration and coherent control we perform here atomistic modeling of the central spin system formed by the electron spin of a silicon vacancy color center ($V_{Si}^-$-center) and the non-interacting nuclear spins. From this we lay out conditions for realizing a scalable nuclear-spin ($^{13}C$ or $^{29}Si$) register, wherein independent control of the qubits alongside their mutual controlled operations using the central electron spin associated to the $V_{Si}^-$-center in 4H-SiC are achieved. Further, the decoherence and entanglement analysis provided here could be used to evaluate the quantum volume of these nodes. Our results mark a clear route towards realizing scalable quantum memory nodes for applications in distributed quantum computing networks and further for quantum information protocols.

Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses on the replacement of neural networks (NN) by variational quantum circuits (VQC) as function approximators. Initial works have shown promising results on classical environments with discrete action spaces, but many of the proposed architectural design choices of the VQC lack a detailed investigation. Hence, in this work we investigate the impact of VQC design choices such as angle embedding, encoding block architecture and postprocessesing on the training capabilities of QRL agents. We show that VQC design greatly influences training performance and heuristically derive enhancements for the analyzed components. Additionally, we show how to design a QRL agent in order to solve classical environments with continuous action spaces and benchmark our agents against classical feed-forward NNs.

A distributed quantum network would require quantum nodes capable of performing arbitrary quantum information protocols with high fidelity. So far the challenge has been in realizing such quantum nodes with features for scalable quantum computing. We show here that using the solid-state spins in 4H-Silicon Carbide (4H-SiC) such a goal could be realized, wherein a controlled generation of highly coherent qubit registers using nuclear spins is possible. Using a controlled isotope concentration and coherent control we perform here atomistic modeling of the central spin system formed by the electron spin of a silicon vacancy color center ($V_{Si}^-$-center) and the non-interacting nuclear spins. From this we lay out conditions for realizing a scalable nuclear-spin ($^{13}C$ or $^{29}Si$) register, wherein independent control of the qubits alongside their mutual controlled operations using the central electron spin associated to the $V_{Si}^-$-center in 4H-SiC are achieved. Further, the decoherence and entanglement analysis provided here could be used to evaluate the quantum volume of these nodes. Our results mark a clear route towards realizing scalable quantum memory nodes for applications in distributed quantum computing networks and further for quantum information protocols.