p-Orbital Qubit
Dr. Jason Kestner, Department of Physics.
This project focuses on a semiconductor based charge qubit proposed in Ref. [Caporaletti 2024, preprint: 2411.06058]. This qubit is advantageous over conventional semiconductor spin qubits because of it’s larger quality factor and higher gate speed. Main computational tasks include simulating the experimental spectroscopy of a multi-electron quantum dot, multi-level Landau-Zener interferometry, and Full Configuration Interaction (FCI) calculations.
Neural Network Enhanced DEER Spectroscopy for Optoelectronic Materials
Jason Kestner, Department of Physics
We propose a physics-informed deep neural network (DNN) approach to design shaped pulses that make spin-spin distance measurements with double electron-electron resonance (DEER) spectroscopy more robust against ensemble dephasing. The bottom-up synthesis of new optoelectronic materials requires the ability to precisely characterize structure on the nanometer scale. DEER would be ideal for this except that it is limited by the ensemble spin dephasing time (T2), which may be on the order of tens of nanoseconds or less in many novel optoelectronic materials compared to a few microseconds in the frozen molecular proteins where DEER is currently most frequently used. However, this is not a fundamental barrier. In top-down, gate-defined lateral quantum dot spin qubits, the coherence can typically be prolonged by several orders of magnitude, close to the coherence time (T2), by the use of multi-pulse dynamical decoupling approaches such as the Carr-Purcell-Meiboom-Gill (CPMG) sequence. While multi-pulse sequences exist for DEER, they generally do not improve performance due to the accumulation of imperfections in the spin rotations that comprise the sequence resulting from non-optimal pulse shapes that do not refocus all resonance heterogeneities. We will address this by using the physics-informed DNN approach to design shaped pulses that enhance sensitivity for DEER and allow access to shorter coherence times. This seed phase will focus on computational modeling, performance optimization, and experimental validation, leveraging the PI’s expertise in neural network pulse shaping and spin qubit control theory and the co-PI’s expertise in coherent spin spectroscopy and spin system modeling.
Randomized Benchmarking for Singlet-Triplet Qubits
Jason Kestner, Physics
Fernando Calderon-Vargas, Physics
Michael Wolfe, Physics
This project will carry out simulated randomized benchmarking to quantify the effects of classical noise on the evolution of a simple two-qubit quantum system during application of dynamically corrected entangling gates. The specific system considered is that of a pair of singlet-triplet qubits defined by a quadruple quantum dot in GaAs.