Topological Photonics Inverse Problem by Machine Learning

Topological concepts open many new horizons for photonic devices, from integrated optics to lasers. The complexity of large scale topological devices asks for an effective solution of the inverse problem: how best to engineer the topology for a specific application? We introduce a novel machine learning approach to the topological inverse problem. We train a neural network system with the band structure of the Aubry-Andre-Harper model and then adopt the network for solving the inverse problem. Our application is able to identify the parameters of a complex topological insulator in order to obtain protected edge states at target frequencies. One challenging aspect is handling the multivalued branches of the direct problem and discarding unphysical solutions. We overcome this problem by adopting a self-consistent method to only select physically relevant solutions. We demonstrate our technique in a realistic topological laser design and by resorting to the widely available open-source TensorFlow library. Our results are general and scalable to thousands of topological components. This new inverse design technique based on machine learning potentially extends the applications of topological photonics, for example, to frequency combs, quantum sources, neuromorphic computing and metrology.

Pilozzi, Farrelly, Marcucci, Conti in ArXiv:1803.02875

Topological Cascade Laser

The cascade of resonantĀ PT-symmetric topological structures is shown to emit laser light with a frequency comb spectrum. We consider optically active topological lattices supporting edge modes at regularly spaced frequencies. When the amplified resonances in the PT-broken regime match the edge modes of the topological gratings, we predict the emission of discrete laser lines. A proper design enables the engineering of the spectral features for specific applications. Topological protection makes the system very well suited for a novel generation of compact frequency comb emitters for spectroscopy, metrology, and quantum information.

Laura Pilozzi and Claudio Conti, Optics Letters 42, 5174 (2017)

Quantum Simulation of Rainbow Gravity

Rainbow gravity modifies general relativity by introducing an energy dependent metric, which is expected to have a role in the quantum theory of black holes and in quantum gravity at Planck energy scale. We show that rainbow gravity can be simulated in the laboratory by nonlinear waves in nonlocal media, as those occurring in Bose-condensed gases and nonlinear optics. We reveal that at a classical level, a nonlocal nonlinear Schr\”odinger equation may emulate the curved space time in proximity of a rotating black hole as dictated by the rainbow gravity scenario. We also demonstrate that a fully quantized analysis is possible. By the positive $\mathcal{P}$-representation, we study superradiance and show that the instability of a black-hole and the existence of an event horizon are inhibited by an energy dependent metric. Our results open the way to a number of fascinating experimental tests of quantum gravity theories and quantum field theory in curved manifolds, and also demonstrate that these theories may be novel tools for open problems in nonlinear quantum physics.

The picture below shows spectra and configuration of particles trapped in a quantum simulation of a black-hole.

Braidotti and Conti, in ArXiv:1708.02623