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

@article{LapointeMajor2019AlgorithmFA,
  title={Algorithm for automated tuning of a quantum dot into the single-electron regime},
  author={M. Lapointe-Major and O. Germain and Julien Camirand Lemyre and Dany Lachance-Quirion and Sophie Rochette and F{\'e}lix Camirand Lemyre and Michel Pioro-Ladri{\`e}re},
  journal={Bulletin of the American Physical Society},
  year={2019},
  volume={2019}
}
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… 

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References

SHOWING 1-10 OF 27 REFERENCES

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

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

Dynamically controlled charge sensing of a few-electron silicon quantum dot

We report charge sensing measurements of a silicon metal-oxide-semiconductor quantum dot using a single-electron transistor as a charge sensor with dynamic feedback control. Using

Fast Sensing of Double-Dot Charge Arrangement and Spin State with a Radio-Frequency Sensor Quantum Dot

Single-shot measurement of the charge arrangement and spin state of a double quantum dot are reported, with measurement times down to ~ 100 ns. Sensing uses radio-frequency reflectometry of a

Computer-automated tuning procedures for semiconductor quantum dot arrays

An image analysis toolbox developed in Python is used to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention, and straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion.

Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks

An algorithm is introduced that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state and consistently arrives at the desired state or its immediate neighborhood.

Quantum computation with quantum dots

We propose an implementation of a universal set of one- and two-quantum-bit gates for quantum computation using the spin states of coupled single-electron quantum dots. Desired operations are

Loading a quantum-dot based “Qubyte” register

Electrostatically defined quantum dot arrays offer a compelling platform for quantum computation and simulation. However, tuning up such arrays with existing techniques becomes impractical when going

Automated tuning of inter-dot tunnel coupling in double quantum dots

Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum

Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots

It is shown that automating well established manual tuning procedures and replacing the experimenter's decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without pre-measured input or manual intervention.

Shuttling a single charge across a one-dimensional array of silicon quantum dots

Significant advances have been made towards fault-tolerant operation of silicon spin qubits, with single qubit fidelities exceeding 99.9%, several demonstrations of two-qubit gates based on exchange