Colloquia & Guest Speakers

Reduction of the Quantum Decoherence in Integrated Photonics by Machine Learning-assisted Design

Professor Pablo A. Postigo, The Institute of Optics, University of Rochester

Monday, January 23, 2023
3:30 p.m.

In-person in Goergen 101 and Zoom

Zoom Information

Meeting ID: 952 7674 7247
Passcode: 964579


The development of on-chip, CMOS-compatible quantum photonics is critical for future scalable quantum communications, quantum computing, and quantum sensing. Integrated photonic waveguides, photonic resonators, and single-photon emitters are essential building blocks for such a purpose. In this talk, I will present how machine learning (ML) can enhance the quantum properties of these building blocks, specifically the indistinguishability (I) of the generated single photons, with a further decrease in quantum decoherence. We have explored the optimization of I for integrated photonic waveguides through an analytical model that uses Green´s Dyadic of a 3D unbounded rectangular waveguide to calculate I and the coupling of an ideal point-source quantum emitter coupled to a photonic waveguide depending on its orientation and position. The model has been numerically evaluated through finite-difference time-domain (FDTD) simulations showing consistent results. Also, we explored a hybrid slot-Bragg nanophotonic cavity to generate indistinguishable photons at RT from various quantum emitters through a combination of numerical methods. To relax the fabrication requirements (slot width) for near-unity I, we used an ML algorithm that provides the optimal geometry of the cavity. Finally, we have developed a theory for estimating I in a two-emitter system with strong dephasing coupled to a single-mode cavity. We have derived an analytical expression of I as a function of the distance between the emitters, cavity decay rate, and pure dephasing rate. The results show how the requirements of the cavity for high I change with the strength of the dipolar interaction. We propose a new interpretation of the I value which allows us to estimate its behavior with larger systems (i.e., systems with more than two emitters). We performed numerical simulations of five dipole-coupled emitters to find the optimal configuration for maximum I. For the optimization process, we developed a novel ML scheme based on a hybrid neural network (NN)-genetic algorithm (GA) to find the position of each emitter to maximize I. The optimization procedure provides perfect I (i.e., I = 1) in arbitrary low Q cavities, offering advantages for relaxing the cavity requirements and favoring the use of quantum emitters at room T.


Headshot of Pablo Postigo.
Professor Pablo A. Postigo

Dr. Pablo Aitor Postigo received his M.Sc. degree in Physics from the University of Basque Country in 1992, and a Ph.D. in Physics from the Polytechnic University of Madrid in 1996. He was a Postdoctoral Associate at the Department of Electrical Engineering of the Massachusetts Institute of Technology (MIT) from 1996 to 1999. He worked in the optoelectronic integration of lasers with VLSI electronics. In 2005, he became a permanent researcher at the Institute of Micro and Nanotechnology (IMN-CSIC) in Spain, where he was the head of the Group of Nanophotonic Devices. His work is focused on the design, fabrication, and characterization of nanophotonic devices, including photonic crystal lasers, single photon emitters, photonic crystal solar cells, and biomedical devices. Among other findings, in 2015 his group demonstrated the first near-thresholdless laser at room temperature. He is co-author of more than 100 international publications and has participated in several European Projects, Networks of Excellence, COST Actions, and US projects supported by AFOSR and DARPA. Since July 2021 he is Professor of Optics at the Institute of Optics of the University of Rochester.