Bridging the reality gap in quantum devices with physics-aware machine learning

Craig, David L; Moon, Hyun-Joo; Fedele, Federico; Lennon, Dominic T.; Straaten, B. Van; Vigneau, Florian; Camenzind, Leon C.; Zumbühl, Dominik M.; Briggs, G. Andrew D.; Osborne, Michael A.; Sejdinović, Dino; Ares, Natalia

doi:10.48550/arxiv.2111.11285

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…This is due to inconsistencies in the physical environment, change in dynamics such as friction, density, collision, battery drainage, wear and tear, sensor noise and mainly the dynamic nature of the environments. Although simulation requires limited system resources, it cannot capture the full complexities of the physical system [3]. Therefore, simulations fail to model different aspects of the environment.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Ranaweera

Mahmoud

2023

IEEE Access

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Creating Reinforcement learning(RL) agents that can perform tasks in the real-world robotic systems remains a challenging task due to inconsistencies between the virtual-and the real-world. This is known as the ''reality-gap'' which hinders the performance of a RL agent trained in a virtual environment. The research describes the techniques used to train the models, generate randomized environments, reward function, and techniques utilized to transfer the model to the physical environment for evaluation. For this investigation, a low-cost 3-degrees-of-freedom (DOF) Steward platform was 3D modeled and created virtually and physically. The goal of the 3D-Stewart platform was to guide and balance the marble towards the center. Custom end-to-end APIs were developed to interact with the Godot game engine, manipulate physics and dynamics, interact with the in-game lighting and perform environment randomizations. Two RL algorithms: Q-learning and Actor-Critic, were implemented to evaluate the performance by using domain randomization and induced noise to bridge the reality gap. For Q-learning, raw frames were used to make predictions while Actor-Critic utilized marble position, velocity vector and relative position by preprocessing captured frames. The experimental results show the effectiveness of domain randomization and introduction of noise during the training.

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Section: Introductionmentioning

confidence: 99%

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Ranaweera

Mahmoud

2023

IEEE Access

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“…A related idea has appeared recently consisting in a proof of principle where the disorder is determined in Majorana nanowire systems [14]. Another application which consists in using machine learning to adjust device parameters to compensate for uncontrolled disorder effects has been recently implemented in the case of a double quantum dot nanostructure [15]. It has also been suggested that properties of the disorder between the fingers of a QPC can be extracted from SGM data using cellular neural networks [16] or a swarming algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy

Percebois,

Lacerda-Santos,

Brun

et al. 2023

SciPost Phys.

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The weak disorder potential seen by the electrons of a two-dimensional electron gas in high-mobility semiconductor heterostructures leads to fluctuations in the physical properties and can be an issue for nanodevices. In this paper, we show that a scanning gate microscopy (SGM) image contains information about the disorder potential, and that a machine learning approach based on SGM data can be used to determine the disorder. We reconstruct the electric potential of a sample from its experimental SGM data and validate the result through an estimate of its accuracy.

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“…We developed a physics-inspired simulator and introduced crossdevice validation to address this challenge. In the context of tuning quantum devices, deep learning has been used for various other tasks [17,19,[23][24][25][26][27], with some approaches using simulated data to train their algorithms [15,16,28].…”

Section: Introductionmentioning

confidence: 99%

Identifying Pauli spin blockade using deep learning

Schuff¹,

Lennon²,

Geyer³

et al. 2022

Preprint

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Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.

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Bridging the reality gap in quantum devices with physics-aware machine learning

Cited by 4 publications

References 30 publications

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy

Identifying Pauli spin blockade using deep learning

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