Algorithm for automated tuning of a quantum dot into the single-electron regime

Abstract

We report an algorithm designed to perform computer-automated tuning of a single quantum dot with a charge sensor. The algorithm performs an adaptive measurement sequence of sub-sized stability diagrams until the single-electron regime is identified and reached. For each measurement, the signal processing module removes the physical background of the charge sensor to generate a binary image of charge transitions. Then, the image analysis module identifies the position and number of lines using two line detection schemes that are robust to noise and missing data.

References in corpus (20)

• Quantum Computation with Quantum Dots
• Silicon Quantum Electronics
• Interfacing spin qubits in quantum dots and donors - hot, dense and coherent
• Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array
• Scalable gate architecture for densely packed semiconductor spin qubits
• Shuttling a single charge across a one-dimensional array of silicon quantum dots
• Fast sensing of double-dot charge arrangement and spin state with an rf sensor quantum dot
• Loading a quantum-dot based "Qubyte" register
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• Auto-tuning of double dot devices in situ with machine learning
• Automated tuning of inter-dot tunnel couplings in quantum dot arrays
• Automated tuning of double quantum dots into specific charge states using neural networks
• Enhancement mode double top gated MOS nanostructures with tunable lateral geometry
• Computer-automated tuning of semiconductor double quantum dots into the single-electron regime
• Computer-automated tuning procedures for semiconductor quantum dot arrays
• Dynamically controlled charge sensing of a few-electron silicon quantum dot
• Autonomous tuning and charge state detection of gate defined quantum dots
• Quantum dots with split enhancement gate tunnel barrier control
• Fabrication of quantum dots in undoped Si/Si${}_{0.8}$Ge${}_{0.2}$ heterostructures using a single metal-gate layer
• Single-Shot Readout Performance of Two Heterojunction-Bipolar-Transistor Amplification Circuits at Millikelvin Temperatures

Cited by in corpus (21)

• Colloquium: Advances in automation of quantum dot devices control
• Toward Robust Autotuning of Noisy Quantum Dot Devices
• Ray-based framework for state identification in quantum dot devices
• Real-Time Feedback Control of Charge Sensing for Quantum Dot Qubits
• Miniaturizing neural networks for charge state autotuning in quantum dots
• Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
• Automated extraction of capacitive coupling for quantum dot systems
• Data needs and challenges for quantum dot devices automation
• Identifying Pauli spin blockade using deep learning
• Automatic virtual voltage extraction of a 2x2 array of quantum dots with machine learning
• Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
• Fully autonomous tuning of a spin qubit
• Visual explanations of machine learning model estimating charge states in quantum dots
• All rf-based tuning algorithm for quantum devices using machine learning
• Experimental online quantum dots charge autotuning using neural networks
• Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams
• Robust quantum dots charge autotuning using neural network uncertainty
• Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering
• Gradient descent in materia through homodyne gradient extraction
• Bootstrapping, autonomous testing, and initialization system for Si/Si${}_x$Ge${}_{1-x}$ multi-quantum-dot devices
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements