Tuning methods for semiconductor spin--qubits

Abstract

We present efficient methods to reliably characterize and tune gate-defined semiconductor spin qubits. Our methods are designed to target the tuning procedures of semiconductor double quantum dot in GaAs heterostructures, but can easily be adapted to other quantum-dot-like qubit systems. These tuning procedures include the characterization of the inter-dot tunnel coupling, the tunnel coupling to the surrounding leads and the identification of the various fast initialization points for the operation of the qubit. Since semiconductor-based spin qubits are compatible with standard semiconductor process technology and hence promise good prospects of scalability, the challenge of efficiently tuning the dot's parameters will only grow in the near future, once the multi-qubit stage is reached. With the anticipation of being used as the basis for future automated tuning protocols, all measurements presented here are fast-to-execute and easy-to-analyze characterization methods. They result in quantitative measures of the relevant qubit parameters within a couple of seconds, and require almost no human interference.

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Cited by in corpus (31)

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• Machine Learning techniques for state recognition and auto-tuning in quantum dots
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• Auto-tuning of double dot devices in situ with machine learning
• Colloquium: Advances in automation of quantum dot devices control
• Computer-automated tuning procedures for semiconductor quantum dot arrays
• Autonomous tuning and charge state detection of gate defined quantum dots
• A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits
• QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
• Control Electronics For Semiconductor Spin Qubits
• High-fidelity gate set for exchange-coupled singlet-triplet qubits
• The effect of valley, spin and band nesting on the electronic properties of gated quantum dots in a single layer of transition metal dichalcogenides (TMDCs)
• Real-time two-axis control of a spin qubit
• An automated approach for consecutive tuning of quantum dot arrays
• Autonomous estimation of high-dimensional Coulomb diamonds from sparse measurements
• Machine learning optimization of Majorana hybrid nanowires
• Identifying Pauli spin blockade using deep learning
• Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
• Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays
• Agents for self-driving laboratories applied to quantum computing
• Towards a realistic GaAs-spin qubit device for a classical error-corrected quantum memory
• Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)
• Topological gap protocol based machine learning optimization of Majorana hybrid wires
• Operating two exchange-only qubits in parallel
• Experimental online quantum dots charge autotuning using neural networks
• Quasi-Perfect State Transfer in Spin Chains via Parametrization of On-Site Energies
• Spin-valley qubits in gated quantum dots in a single layer of transition metal dichalcogenides
• Interplay of Pauli blockade with electron-photon coupling in quantum dots
• State transfer analysis for linear spin chains with non-uniform on-site energies