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Gupta, Shobhit

Publications and source records attributed to Gupta, Shobhit.

Isolation of individual Er quantum emitters in anatase TiO 2 on Si photonics

Defects and dopant atoms in solid state materials are a promising platform for realizing single photon sources and quantum memories, which are the basic building blocks of quantum repeaters needed for long distance quantum networks. In particular, trivalent erbium (Er 3+ ) is of interest because it couples C-band telecom optical transitions with a spin-based memory platform. In order to produce quantum repeaters at the scale required for quantum networks it is imperative to integrate these necessary building blocks with mature and scalable semiconductor processes. Here, in this work, we demonstrate the optical isolation of single Er 3+ ions in CMOS-compatible titanium dioxide (TiO 2 ) thin films monolithically integrated on a silicon-on-insulator photonics platform. Our results demonstrate an initial step toward the realization of a monolithically integrated and scalable quantum photonics package based on Er 3+ doped thin films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Robust Millisecond Coherence Times of Erbium Electron Spins

Erbium doped solids are prime candidates for quantum memories in optical quantum networks given their telecom-compatible photon emission. An electron spin of erbium with millisecond coherence time is desirable for generating remote entanglement between adjacent quantum network nodes. Here, in this study, we report GHz-range electron spin transitions of 167 Er 3+ in yttrium oxide (Y 2 O 3 ) matrix with coherence times that are consistently longer than a millisecond. By polarizing para magnetic impurity spins we achieve a spin T 2 up to 1.46 ms, and up to 7.1 ms after dynamical decoupling. These coherence lifetimes are among the longest found for erbium in crystalline matrices despite the presence of host nuclear spins. We further enhance the coherence time beyond conventional dynamical decoupling, using customized sequences to simultaneously mitigate spectral diffusion and dipolar interactions. Our study not only establishes 167 Er 3+ : Y 2 O 3 as a significant quantum memory platform but also provides a guideline for engineering long-lived erbium spins in a variety of host materials for quantum technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Real-Time Ecodriving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programing

Connected and automated vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the ecodriving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multilayer model predictive control (MPC) approach for improving the fuel economy of a 48 V mild-hybrid powertrain in a connected vehicle environment. Approximate dynamic programing (DP) is used to solve the receding horizon optimal control problem, whose terminal cost is approximated with the base policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Automation & Control Systems↗

Koopman Model Predictive Control for Eco-Driving of Automated Vehicles

In this paper, we develop a data-driven process for building a model predictive control (MPC) for eco-driving of automated vehicles. The process involves performing system identification in which the non-linear vehicle dynamics model is approximated by the Koopman operator, a linear predictor of higher state-dimension, in a data-driven framework. This approach allows us to formulate the eco-driving problem in a constrained quadratic program that leads to a computationally fast MPC. The MPC is then implemented as a closed-loop control of an electric vehicle in numerical simulations for demonstration.

autonomous vehicle↗