Physics-informed simulation, parameter-estimation, and optimization workflows for quantum-device experiments.
This project collects physics-informed computational modeling work used to support experiment design and interpretation in semiconductor spin-qubit systems. The models range from effective-Hamiltonian simulations to device-level workflows connecting lithographic layouts, electron wavefunctions, micromagnetic fields, and qubit dynamics.
One direction built a Python-based simulation framework for estimating dynamic nuclear polarization rates in lateral GaAs quantum dots using a modified Hubbard-model picture with excited-state spin mixing. The simulations helped explain bidirectional nuclear polarization behavior observed experimentally.
Another direction used QuTiP to solve quantum master equations with Lindblad operators for singlet-triplet qubits interacting with nearby many-electron dot states in Si/SiGe devices. These simulations reproduced beating and dephasing signatures in Ramsey measurements and helped interpret coherent qubit-environment interactions.
The project also includes data-analysis and estimation routines for device screening, qubit calibration, and performance characterization, as well as workflows that start from device geometry, compute electron wavefunctions, combine them with MuMax3-simulated magnetic fields, and estimate quantities relevant to spin-qubit control.
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