Machine learning techniques for state recognition and auto-tuning in quantum dots

Kalantre, Sandesh S.; Zwolak, Justyna P.; Ragole, Stephen; Wu, Xingyao; Zimmerman, Neil M.; Stewart, M. D.; Taylor, Jacob M.

doi:10.1038/s41534-018-0118-7

Cited by 79 publications

(75 citation statements)

References 46 publications

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“…The value of machine learning in finding patterns and optima in data which depends on many parameters is apparent across multiple fields of research [40]. In our specific case, machine learning has provided a means for autonomous experimental optimization.…”

Section: Resultsmentioning

confidence: 99%

“…To incorporate the non-linearity of the cost function, we include the Gaussian error linear unit (GELU) activation function for each node [39]. This continuous function is a popular choice for data which is subject to normally-distributed stochastic variation, which suits our experimental context [40]. In addition, the structure and scale of the ANN must be appropriate for the complexity and size of the vector inputs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Applying machine learning optimization methods to the production of a quantum gas

Barker

Style

Luksch

et al. 2020

Mach. Learn.: Sci. Technol.

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We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.

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Section: Resultsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Applying machine learning optimization methods to the production of a quantum gas

Barker

Style

Luksch

et al. 2020

Mach. Learn.: Sci. Technol.

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“…More and more nanomaterial‐related databases are available, such as Nanoparticle Information Library (NIL), Nano‐HUB database, and Investigation Study Assay tab‐delimited format (ISA‐TAB‐Nano) . Moreover, with the improvement of computational capabilities and algorithms, machine learning is paving a promising path to accelerating nanomaterials discovery, design and applications ( Figure ) . Although machine learning has shown the capabilities of making predictions with little human input, high time‐efficiency and performance‐efficiency, it also creates new challenges in guaranteeing the prediction accuracy for nanomaterials science.…”

Section: Introductionmentioning

confidence: 99%

“…Published by Nature Publishing Group. State recognition and autotuning in QDs: Reproduced with permission under the terms of the Creative Commons Attribution 4.0 International License . Copyright 2019, Nature Publishing Group.…”

Section: Introductionmentioning

confidence: 99%

Nanomaterials Discovery and Design through Machine Learning

Wang

Cai

et al. 2019

Small Methods

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“…One of the key challenges in scaling up spin qubits is developing the software tools necessary to keep pace with increasingly complex devices. To date, approaches to implementing automated control software during tune-up of semiconductor qubits include training neural networks to identify the state of a device 16 , experimentally realizing automated control procedures for tuning double quantum dot (DQD) devices into the single-electron regime 17 , and automatically tuning the interdot tunnel coupling in a DQD [18][19][20] .…”

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confidence: 99%

Computer-automated tuning procedures for semiconductor quantum dot arrays

Mills

Feldman

Monical

et al. 2019

Applied Physics Letters

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As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a 'tunnel coupling lever arm' to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the GHz values that are compatible with exchange gates.Quantum processors rely on classical hardware and controls in order to prepare, manipulate, and measure qubit states. For this reason, it is advantageous to develop tools to automate the operation of small quantum processors and routinely tune-up single qubit and two-qubit gates to maintain high performance 1-3 . Semiconductor spin qubits are a promising platform for realizing quantum computation largely due to their potential for scaling 4 . To tune up semiconductor quantum dots for operation as spin qubits requires control over the ground state charge occupation and chemical potential of each dot, as well as the interdot tunnel couplings 5 .Following the recent progress in constructing high-fidelity single-qubit and two-qubit gate operations with electron spins 6-11 , there are increasing efforts towards scaling to larger multi-qubit devices 12-15 . One of the key challenges in scaling up spin qubits is developing the software tools necessary to keep pace with increasingly complex devices. To date, approaches to implementing automated control software during tune-up of semiconductor qubits include training neural networks to identify the state of a device 16 , experimentally realizing automated control procedures for tuning double quantum dot (DQD) devices into the single-electron regime 17 , and automatically tuning the interdot tunnel coupling in a DQD [18][19][20] .In this Letter, we use an image analysis toolbox developed at Sandia National Laboratories to accurately analyze charge stability diagrams acquired from a triple quantum dot (TQD) unit cell of a 9-dot linear array 13,21 . Computer automated analysis of charge stability diagrams performs the inversion of the device capacitance matrix and the establishment of 'virtual gates'. Virtual gates compensate for cross-capacitances in the device and allow the chemical potential of each dot in the array to be independently controlled 13,22,23 . Furthermore, we use image analysis to locate interdot charge transitions and automatically perform measurements of the interdot tunnel coupling 24 . Using simple feedback protocols, we demonstrate simultaneous tune-up of the i...

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Machine learning techniques for state recognition and auto-tuning in quantum dots

Cited by 79 publications

References 46 publications

Applying machine learning optimization methods to the production of a quantum gas

Applying machine learning optimization methods to the production of a quantum gas

Nanomaterials Discovery and Design through Machine Learning

Computer-automated tuning procedures for semiconductor quantum dot arrays

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