Colloquium: Advances in automation of quantum dot devices control

Abstract

Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.

24 pages, 11 figures

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• Toward Robust Autotuning of Noisy Quantum Dot Devices
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Cited by in corpus (30)

• Optimizing quantum gates towards the scale of logical qubits
• A 300 mm foundry silicon spin qubit unit cell exceeding 99% fidelity in all operations
• Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
• Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays
• Key Issues Review: Useful autonomous quantum machines
• Automated extraction of capacitive coupling for quantum dot systems
• Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
• Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
• Data needs and challenges for quantum dot devices automation
• Qubit-controlled directional edge states in waveguide QED
• Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
• Autonomous Bootstrapping of Quantum Dot Devices
• Visual explanations of machine learning model estimating charge states in quantum dots
• Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning
• Theory of charge stability diagrams in coupled quantum dot qubits
• On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots
• Leveraging Off-the-Shelf Silicon Chips for Quantum Computing
• Differentiable master equation solver for quantum device characterisation
• 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
• Automated in situ optimization and disorder mitigation in a quantum device
• Current-based metrology with two-terminal mesoscopic conductors
• Bootstrapping, autonomous testing, and initialization system for Si/Si${}_x$Ge${}_{1-x}$ multi-quantum-dot devices
• Neural network enhanced cross entropy benchmark for monitored circuits
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
• AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
• Energy Dynamics of a Nonequilibrium Unitary Fermi Gas
• Exploring Entanglement Spectrum and Phase Diagram in multi-electron Quantum Dot Chains
• Global-Local Duality of Energetic Control Cost in Multipartite Quantum Correlated Systems