Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

Abstract

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a modular automated virtualization system (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multilayer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.

14 pages, 5 figures, 9 pages of supplemental material

References in corpus (45)

• An addressable quantum dot qubit with fault-tolerant control fidelity
• A >99.9%-fidelity quantum-dot spin qubit with coherence limited by charge noise
• Semiconductor Spin Qubits
• Interfacing spin qubits in quantum dots and donors - hot, dense and coherent
• Fast universal quantum control above the fault-tolerance threshold in silicon
• Universal control of a six-qubit quantum processor in silicon
• A four-qubit germanium quantum processor
• The germanium quantum information route
• Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array
• Two-qubit silicon quantum processor with operation fidelity exceeding 99%
• Realization of a minimal Kitaev chain in coupled quantum dots
• Fast two-qubit logic with holes in germanium
• Ge hole spin qubit
• Coherent manipulation of an Andreev spin qubit
• Low disordered, stable, and shallow germanium quantum wells: a playground for spin and hybrid quantum technology
• Probing single electrons across 300 mm spin qubit wafers
• Gate-controlled quantum dots and superconductivity in planar germanium
• Universal logic with encoded spin qubits in silicon
• Direct manipulation of a superconducting spin qubit strongly coupled to a transmon qubit
• Loading a quantum-dot based "Qubyte" register
• Probing quantum devices with radio-frequency reflectometry
• Quantum tomography of an entangled three-spin state in silicon
• Fast charge sensing of a cavity-coupled double quantum dot using a Josephson parametric amplifier
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• High-fidelity operation and algorithmic initialisation of spin qubits above one kelvin
• Machine learning enables completely automatic tuning of a quantum device faster than human experts
• A two-site Kitaev chain in a two-dimensional electron gas
• Operating semiconductor quantum processors with hopping spins
• Auto-tuning of double dot devices in situ with machine learning
• Automated tuning of double quantum dots into specific charge states using neural networks
• Colloquium: Advances in automation of quantum dot devices control
• Computer-automated tuning procedures for semiconductor quantum dot arrays
• Germanium wafers for strained quantum wells with low disorder
• Efficient orthogonal control of tunnel couplings in a quantum dot array
• A two-dimensional array of single-hole quantum dots
• QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
• Algorithm for automated tuning of a quantum dot into the single-electron regime
• Coherent multi-spin exchange coupling in a quantum-dot spin chain
• Ray-based framework for state identification in quantum dot devices
• Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
• Exciton transport in a germanium quantum dot ladder
• Automated extraction of capacitive coupling for quantum dot systems
• Transmission phase read-out of a large quantum dot in a nanowire interferometer
• Autonomous Bootstrapping of Quantum Dot Devices
• A quantitative study of bias triangles presented in chemical potential space

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• QDFlow: A Python package for physics simulations of quantum dot devices
• Comparison of spin-qubit architectures for quantum error-correcting codes