Tunable and robust long-range coherent interactions between quantum emitters mediated by Weyl bound states

Long-range coherent interactions between quantum emitters are instrumental for quantum information and simulation technologies, but they are generally difficult to isolate from dissipation. Here, we show how such interactions can be obtained in photonic Weyl environments due to the emergence of an exotic bound state whose wavefunction displays power-law spatial confinement. Using an exact formalism, we show how this bound state can mediate coherent transfer of excitations between emitters, with virtually no dissipation and with a transfer rate that follows the same scaling with distance as the bound state wavefunction. In addition, we show that the topological nature of Weyl points translates into two important features of the Weyl bound state, and consequently of the interactions it mediates: first, its range can be tuned without losing the power-law confinement, and, second, they are robust under energy disorder of the bath. To our knowledge, this is the first proposal of a photonic setup that combines simultaneously coherence, tunability, long-range, and robustness to disorder. These findings could ultimately pave the way for the design of more robust long-distance entanglement protocols or quantum simulation implementations for studying long-range interacting systems

Generative adversarial networks for data-scarce spectral applications

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization

Wave-front phase-modulation control and focusing of second-harmonic light generated in transparent nonlinear random structures

We theoretically investigate how phase-only spatial light modulation can enable controlling and focusing the second-harmonic light generated in transparent nonlinear random structures. The studied structures are composed of domains with random sizes and antiparallel polarization, which accurately model widely used ferroelectric crystals such as strontium barium niobate. Using a first-principles Green-function formalism, we account for the effect that spatial light modulation of the fundamental beam introduces into the second-order nonlinear frequency conversion occurring in the considered class of structures. This approach provides a complete description of the physical origin of the second-harmonic light generation in the system, as well as the optimization of the light intensity in any arbitrary direction. Our numerical results show how the second-harmonic light is influenced by both the disorder in the structure and the boundaries of the crystal. Particularly, we find that the net result from the interplay between disorder and boundary effects is strongly dependent on the dimensions of the crystal and the observation direction. Remarkably, our calculations also show that although in general the maximum possible enhancement of the second-order light is the same as the one corresponding to linear light scattering in turbid media, in the Cerenkov phase matching direction the enhancement can exceed the linear limit. The theoretical analysis presented in this work expands the current understanding of light control in complex media and could contribute to the development of a new class of imaging and focusing techniques based on nonlinear frequency mixing in random optical materials.Peer ReviewedPostprint (published version

Deep learning for the modeling and inverse design of radiative heat transfer

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines, including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training data sets, we demonstrate this approach in the context of three very different problems, namely (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural-network architectures trained with data sets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio

Tunable Thermal Emission of Subwavelength Silica Ribbons

The thermal properties of individual subwavelength objects, which defy Planck’s law, are attracting significant fundamental and applied interest in different research areas. Special attention has been devoted to anisotropic structures made of polar dielectrics featuring thicknesses smaller than both the thermal wavelength and the skin depth. Recently, a novel experimental technique has enabled the measurement of the thermal emissivity of anisotropic SiO2 nanoribbons (with thicknesses on the order of 100 nm), demonstrating that their emission properties can be largely tuned by adjusting their dimensions. However, despite the great interest aroused by these results, their rigorous theoretical analysis has remained elusive due to the computational challenges arising from the vast difference in the length scales involved in the problem. In this work, we present a systematic theoretical analysis of the thermal emission properties of these dielectric nanoribbons based on simulations within the framework of fluctuational electrodynamics carried out with the boundary element method implemented in the SCUFF-EM code. In agreement with the experiments, we show that the emissivity of these subwavelength structures can be largely tuned and enhanced over the thin-film limit. We elucidate that the peculiar emissivity of these nanoribbons is due to the very anisotropic thermal emission that originates from the phonon polaritons of this material and the properties of the waveguide modes sustained by these dielectric structures. Our work illustrates the rich thermal properties of subwavelength objects, as well as the need for rigorous theoretical methods that are able to unveil the complex thermal emission phenomena emerging in this class of systemsJ.J.G.E. was supported by the Spanish Ministry of Science and Innovation through an FPU grant (FPU19/05281). J.B.A. acknowledges financial support from the Ministerio de Ciencia, Innovacioń y Universidades (RTI2018-098452-B-I00). J.C.C. acknowledges funding from the Spanish Ministry of Science and Innovation (PID2020-114880GB-I00

Weyl points in photonic-crystal superlattices

We show that Weyl points can be realized in all-dielectric superlattices based on three- dimensional (3D) layered photonic crystals. Our approach is based on creating an inversion- breaking array of weakly-coupled planar defects embedded in a periodic layered structure with a large omnidirectional photonic band gap. Using detailed band structure calculations and tight- binding theory arguments, we demonstrate that this class of layered systems can be tailored to display 3D linear point degeneracies between two photonic bands, without breaking time- reversal symmetry and using a configuration that is readily-accessible experimentally. These results open new prospects for the observation of Weyl points in the near-infrared and optical regimes and for the application of Weyl-physics in integrated photonic devices

Probing and harnessing photonic Fermi arc surface states using light-matter interactions

Fermi arcs, i.e., surface states connecting topologically distinct Weyl points, represent a paradigmatic manifestation of the topological aspects of Weyl physics. We investigate a light-matter interface based on the photonic counterpart of these states and prove that it can lead to phenomena with no analog in other setups. First, we show how to image the Fermi arcs by studying the spontaneous decay of one or many emitters coupled to the system's border. Second, we demonstrate that, exploiting the negative refraction of these modes, the Fermi arc surface states can act as a robust quantum link, enabling, e.g., the occurrence of perfect quantum state transfer between the considered emitters or the formation of highly entangled states. In addition to their fundamental interest, our findings evidence the potential offered by the photonic Fermi arc light-matter interfaces for the design of more robust quantum technologiesI.G.-E. acknowledges financial support from the Spanish Ministry for Science, Innovation, and Universities through FPU grant AP-2018-02748. A.G.-T. acknowledges financial support from the Proyecto Sinérgico CAM 2020 Y2020/TCS-6545 (NanoQuCo-CM), from the CSIC Interdisciplinary Thematic Platform (PTI) Quantum Technologies (PTI-QTEP+), from Spanish project PID2021-127968NB-I00 and the project TED2021-130552B-C22 funded by MCIN/AEI/ 10.13039/ 501100011033/FEDER, UE, and MCIN/AEI/ 10.13039/501100011033, respectively, and the support from a 2022 Leonardo Grant for Researchers and Cultural Creators, BBVA. J.B.-A. and J.M. acknowledge financial support from the Spanish Ministry for Science, Innovation, and Universities through grants RTI2018-098452-B-I00 (MCIU/AEI/FEDER,UE) and MDM-2014-0377 (María de Maeztu programme for Units of Excellence in R&