A5002812934- Quantum and electron transport phenomena
- Advancements in Semiconductor Devices and Circuit Design
- Quantum Computing Algorithms and Architecture
- Diamond and Carbon-based Materials Research
- Semiconductor materials and devices
- Neural Networks and Applications
- Radioactive element chemistry and processing
- Military Strategy and Technology
- Statistical Mechanics and Entropy
- Military Defense Systems Analysis
- Statistical Distribution Estimation and Applications
- Radioactive contamination and transfer
- Machine Learning in Materials Science
- Technology and Data Analysis
- Coal and Its By-products
- Neural Networks and Reservoir Computing
- Analog and Mixed-Signal Circuit Design
- Engineering Applied Research
- Soil and Environmental Studies
- Isotope Analysis in Ecology
- Medical Imaging Techniques and Applications
- Quantum Information and Cryptography
- Simulation Techniques and Applications
- Probabilistic and Robust Engineering Design
- Integrated Circuits and Semiconductor Failure Analysis
University of Oxford
2019-2024
Korea Advanced Institute of Science and Technology
1995-2017
Abstract Scalable quantum technologies such as computers will require very large numbers of devices to be characterised and tuned. As the number on chip increases, this task becomes ever more time-consuming, intractable a scale without efficient automation. We present measurements dot device performed by machine learning algorithm in real time. The selects most informative perform next combining information theory with probabilistic deep-generative model that can generate full-resolution...
Abstract Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar the way humans experience. It offers many advantages in automating decision processes navigate large parameter spaces. This paper proposes efficient measurement of quantum devices based on deep learning. We focus double dot devices, demonstrating fully automatic identification specific transport features called bias triangles. Measurements...
Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at cost large parameter space that has to be explored in order find optimal operating conditions. We demonstrate statistical tuning algorithm navigates this entire space, using just few modelling assumptions, search specific electron transport features. focused on gate-defined dot devices, demonstrating fully automated two different devices double regimes an up...
The discrepancies between reality and simulation impede the optimization scalability of solid-state quantum devices. Disorder induced by unpredictable distribution material defects is one major contributions to gap. We bridge this gap using physics-aware machine learning, in particular, an approach combining a physical model, deep Gaussian random field, Bayesian inference. This enables us infer disorder potential nanoscale electronic device from electron-transport data. inference validated...
Abstract Quantum devices with a large number of gate electrodes allow for precise control device parameters. This capability is hard to fully exploit due the complex dependence these parameters on applied voltages. We experimentally demonstrate an algorithm capable fine-tuning several at once. The acquires measurement and assigns it score using variational auto-encoder. Gate voltage settings are set optimize this in real-time unsupervised fashion. report times double quantum dot within...
A soil fulvic acid was extracted from topsoil of the Okchun Basin, Republic Korea, and purified characterized by chemical methods (elemental analysis, number averaged molecular weight) 13C 1H NMR spectroscopy. An "average" structure a has been developed modifying one structural models proposed for Suwannee River acid, IHSS (International Humic Substances Society) reference sample, based on comparative data. The assignment moieties facilitated use set subspectra CHn (n = 0 to 3) groups,...
There have been many research literature on traditional direct fire combat modelling. Recently, network centric warfare (NCW) is an active topic, in which information plays more important role than the warfare. It can be easily agreed that use of affects results greatly. However, it not straightforward to measure effect information, thus decision making involving impact during a non-trivial task. In this study, we propose simple model for NCW modified from original Lanchester differential...
The discrepancies between reality and simulation impede the optimisation scalability of solid-state quantum devices. Disorder induced by unpredictable distribution material defects is one major contributions to gap. We bridge this gap using physics-aware machine learning, in particular, an approach combining a physical model, deep Gaussian random field, Bayesian inference. This has enabled us infer disorder potential nanoscale electronic device from electron transport data. inference...
To solve nonlinear partial differential equations (PDEs) is one of the most common but important tasks in not only basic sciences also many practical industries. We here propose a quantum variational (QuVa) PDE solver with aid machine learning (ML) schemes to synergise two emerging technologies mathematically hard problems. The core processing this calculate efficiently expectation value specially designed operators. For large system, we obtain data from measurements few control qubits avoid...
We propose a new approach to the stochastic version of Lanchester model. Commonly used model is through Markov-chain method. The approach, however, not appropriate high dimensional heterogeneous force case because large computational cost. In this paper, we an approximation method By matching first and second moments, distribution each unit strength can be approximated with multivariate normal distribution. evaluate discrete by measuring Kullback-Leibler divergence. confirmed accuracy...
The potential of Si and SiGe-based devices for the scaling quantum circuits is tainted by device variability. Each needs to be tuned operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, capable tuning 4-gate FinFET, 5-gate GeSi nanowire 7-gate SiGe heterostructure double dot from scratch. achieve times 30, 10, 92 minutes, respectively. also provides insight into parameter space landscape each these devices. These results...
An amendment to this paper has been published and can be accessed via a link at the top of paper.
Combat modeling is a key area of military science and related research. Here, we propose moment matching scheme with modified stochastic Lanchester-type model. An experiment shows that the proposed makes approximations more rapidly while maintaining high level accuracy compare to Markovian