Computer-automated tuning of semiconductor double quantum dots into the single-electron regime

Abstract

We report the computer-automated tuning of gate-defined semiconductor double quantum dots in GaAs heterostructures. We benchmark the algorithm by creating three double quantum dots inside a linear array of four quantum dots. The algorithm sets the correct gate voltages for all the gates to tune the double quantum dots into the single-electron regime. The algorithm only requires (1) prior knowledge of the gate design and (2) the pinch-off value of the single gate $T$ that is shared by all the quantum dots. This work significantly alleviates the user effort required to tune multiple quantum dot devices.

References in corpus (14)

• Spins in few-electron quantum dots
• Silicon Quantum Electronics
• Coherent control of a single electron spin with electric fields
• An addressable quantum dot qubit with fault-tolerant control fidelity
• Electrical control of a long-lived spin qubit in a Si/SiGe quantum dot
• Prospects for Spin-Based Quantum Computing
• Self-Consistent Measurement and State Tomography of an Exchange-Only Spin Qubit
• Fast sensing of double-dot charge arrangement and spin state with an rf sensor quantum dot
• Electron-nuclear interaction in 13C nanotube double quantum dots
• Semiconductor double quantum dot micromaser
• Single-Spin CCD
• Fast charge sensing of a cavity-coupled double quantum dot using a Josephson parametric amplifier
• Single to quadruple quantum dots with tunable tunnel couplings
• A few-electron quadruple quantum dot in a closed loop

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