Corpus ID: 236772908

Toward Robust Autotuning of Noisy Quantum Dot Devices

@article{Ziegler2021TowardRA,
  title={Toward Robust Autotuning of Noisy Quantum Dot Devices},
  author={Joshua Ziegler and Thomas McJunkin and Emily S. Joseph and Sandesh S. Kalantre and Benjamin Harpt and Donald E. Savage and Max G. Lagally and M. A. Eriksson and Jacob M. Taylor and Justyna P. Zwolak},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.00043}
}
Joshua Ziegler, ∗ Thomas McJunkin, 2 E. S. Joseph, Sandesh S. Kalantre, 4 Benjamin Harpt, D. E. Savage, M. G. Lagally, M. A. Eriksson, Jacob M. Taylor, 3, 4 and Justyna P. Zwolak † National Institute of Standards and Technology, Gaithersburg, MD 20899, USA Department of Physics, University of Wisconsin-Madison, WI 53706, USA Joint Quantum Institute, University of Maryland, College Park, MD 20742, USA Joint Center for Quantum Information and Computer Science, University of Maryland, College Park… Expand

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References

SHOWING 1-10 OF 56 REFERENCES
1 / f noise: Implications for solid-state quantum information
The efficiency of the future devices for quantum information processing will be limited mostly by the finite decoherence rates of the individual qubits and quantum gates. Recently, substantialExpand
Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks
While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. ThisExpand
Autotuning of double dot devices in situ with machine learning.
TLDR
It is shown that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. Expand
Machine learning techniques for state recognition and auto-tuning in quantum dots
TLDR
This work proposes a new paradigm for fully automated experimental initialization through a closed-loop system relying on machine learning and optimization techniques, and develops an approach based on convolutional neural networks which is able to “navigate” the huge space of parameters that characterize a complex, quantum system with neither human guidance nor reliance on a detailed description of the device. Expand
Universal quantum logic in hot silicon qubits
TLDR
The demonstration of ‘hot’ and universal quantum logic in a semiconductor platform paves the way for quantum integrated circuits that host both the quantum hardware and its control circuitry on the same chip, providing a scalable approach towards practical quantum information processing. Expand
Dephasing of Si spin qubits due to charge noise
Spin qubits in silicon quantum dots can have long coherence times, yet their manipulation relies on the exchange interaction, through which charge noise can induce decoherence. Charge traps near theExpand
Operation of a silicon quantum processor unit cell above one kelvin
TLDR
This work indicates that a spin-based quantum computer could be operated at increased temperatures in a simple pumped 4 He system (which provides cooling power orders of magnitude higher than that of dilution refrigerators), thus potentially enabling the integration of classical control electronics with the qubit array. Expand
Computer-automated tuning procedures for semiconductor quantum dot arrays
TLDR
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. Expand
Low-frequency charge noise in Si/SiGe quantum dots
Electron spins in silicon have long coherence times and are a promising qubit platform. However, electric field noise in semiconductors poses a challenge for most single- and multi-qubit operationsExpand
Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots
TLDR
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. Expand
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