Simulating spin qubits in silicon quantum dots
Zach Merino
Spin qubits in semiconductor quantum dots are a promising platform for scalable fault-tolerant quantum computing. However, the fabrication and control of such large-scale quantum processors present significant engineering challenges, including precise device geometry design and optimal tuning of spin and charge states.
To address these challenges, we develop a simulation tool that accelerates quantum device design iteration prior to fabrication. By leveraging three-dimensional Poisson simulations, we extract electrostatic potential landscapes of isolated quantum dots to compute spin and Hubbard Hamiltonian effective parameters, enabling efficient modeling of linear quantum dot arrays.
Precise spin qubit control requires a detailed mapping between experimental parameters—such as electrode voltages and radio-frequency magnetic fields—and effective qubit properties, including the electronic g-factor, exchange energy, and chemical potential. This mapping enables the application of various optimal qubit control strategies, such as GRAPE or in-house-developed reverse engineering techniques for time-ordered Hamiltonians. This lightweight simulation framework facilitates the design of experimental voltage pulses needed to control Hamiltonian parameters, ensuring that simulated results align with experimental expectations.
These results serve as figures of merit for device characterization and the implementation of single- and two-qubit gate operations forming a universal gate set. Such operations are essential for constructing entangled states within a node-based quantum network and performing surface code error correction protocols.
Location
QNC 1201