arXiv subjects

H. Moon

Publications and source records attributed to H. Moon.

11 recordsLinked to original sources

arXiv2024

Tuning the shear and extensional rheology of semi-flexible polyelectrolyte solutions

Semi-flexible polyelectrolytes are a group of biopolymers with a wide range of applications from drag reducing agents in turbulent flows to thickening agents in food and cosmetics. In this study, we investigate the rheology of aqueous solutions of xanthan gum as a canonical semi-flexible polyelectrolyte in steady shear and transient extensional flows via torsional rheometry and dripping-onto-substrate (DoS), respectively. The high molecular weight of the xanthan gum and the numerous charged groups on the side branches attached to the backbone allow the shear and extensional rheology of the xanthan gum solutions to be tuned over a wide range by changing the ionic strength of the solvent. In steady shear flow, increasing the xanthan gum concentration increases both the zero shear viscosity and the extent of shear-thinning of the solution. Conversely, increasing the ionic strength of the solvent by addition of sodium chloride (NaCl) decreases both the zero shear viscosity and the level of shear-thinning. In transient extensional flow, increasing the xanthan gum concentration changes the dynamics of the capillary thinning from an inelastic power-law (IP) response to an elastocapillary (EC) balance, from which an extensional relaxation time can be measured based on the rate of filament thinning. Increasing the NaCl concentration decreases the extensional relaxation time and the transient extensional viscosity of the viscoelastic solution. Based on the dynamics of capillary thinning observed in the DoS experiments, we provide a relationship for the smallest extensional relaxation time that can be measured using DoS. We suggest that the change in the dynamics of capillary thinning from an IP response to an EC response can be used as an easy and robust experimental method for identifying the rheologically effective overlap concentration of a semi-flexible polyelectrolyte solution, i.e., the critical concentration at which polymer molecules start to interact with each other to produce a viscoelastic strain-stiffening response (often perceived as "stringiness") in transient extensional flows such as those involved in dripping, dispensing and filling operations.

cond-mat.soft↗

arXiv2021

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

The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach has enabled us to infer the disorder potential of a nanoscale electronic device from electron transport data. This inference is validated by verifying the algorithm's predictions about the gate voltage values required for a laterally-defined quantum dot device in AlGaAs/GaAs to produce current features corresponding to a double quantum dot regime.

cond-mat.mes-hall↗

arXiv2021

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

cond-mat.mes-hall↗

arXiv2020

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

cond-mat.mes-hall↗

arXiv2020

Semimetal to semiconductor transition in $\text{Bi}/\text{TiO}_{2}$ core/shell nanowires

We demonstrate the full thermoelectric and structural characterization of individual bismuth-based (Bi-based) core/shell nanowires. The influence of strain on the temperature dependence of the electrical conductivity, the absolute Seebeck coefficient and the thermal conductivity of bismuth/titanium dioxide ($\text{Bi}/\text{TiO}_{2}$) nanowires with different diameters is investigated and compared to bismuth (Bi) and bismuth/tellurium (Bi/Te) nanowires and bismuth bulk. Scattering at surfaces, crystal defects and interfaces between the core and the shell reduces the electrical conductivity to less than $5\,\%$ and the thermal conductivity to less than $25\,\%$ to $50\,\%$ of the bulk value at room temperature. On behalf of a compressive strain, $\text{Bi}/\text{TiO}_{2}$ core/shell nanowires show a decreasing electrical conductivity with decreasing temperature opposed to that of Bi and Bi/Te nanowires. We find that the compressive strain induced by the $\text{TiO}_{2}$ shell can lead to a band opening of bismuth increasing the absolute Seebeck coefficient by $10\,\%$ to $30\,\%$ compared to bulk at room temperature. In the semiconducting state, the activation energy is determined to $\left|41.3\pm0.2\right|\,\text{meV}$. We show that if the strain exceeds the elastic limit the semimetallic state is recovered due to the lattice relaxation.

physics.app-ph↗

arXiv2020

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

cond-mat.mes-hall↗

arXiv2020

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explored in order to find the optimal operating conditions. We demonstrate a statistical tuning algorithm that navigates this entire parameter space, using just a few modelling assumptions, in the search for specific electron transport features. We focused on gate-defined quantum dot devices, demonstrating fully automated tuning of two different devices to double quantum dot regimes in an up to eight-dimensional gate voltage space. We considered a parameter space defined by the maximum range of each gate voltage in these devices, demonstrating expected tuning in under 70 minutes. This performance exceeded a human benchmark, although we recognise that there is room for improvement in the performance of both humans and machines. Our approach is approximately 180 times faster than a pure random search of the parameter space, and it is readily applicable to different material systems and device architectures. With an efficient navigation of the gate voltage space we are able to give a quantitative measurement of device variability, from one device to another and after a thermal cycle of a device. This is a key demonstration of the use of machine learning techniques to explore and optimise the parameter space of quantum devices and overcome the challenge of device variability.

cond-mat.mes-hall↗

arXiv2018

Efficiently measuring a quantum device using machine learning

Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of automation. We present measurements on a quantum dot device performed by a machine learning algorithm. The algorithm selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model, the latter capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead presents the use of algorithms to automate measurement. This work lays the foundation for automated control of large quantum circuits.

quant-ph↗

arXiv2017

Relativistic Extension of a Charge-Conservative Finite Element Solver for Time-Dependent Maxwell-Vlasov Equations

In many problems involving particle accelerators and relativistic plasmas, the accurate modeling of relativistic particle motion is essential for accurate physical predictions. Here, we extend a charge-conserving finite element time-domain (FETD) particle-in-cell (PIC) algorithm for the time-dependent Maxwell-Vlasov equations on irregular (unstructured) meshes to the relativistic regime by implementing and comparing three particle pushers: (relativistic) Boris, Vay, and Higuera-Cary. We illustrate the application of the proposed relativistic FETD-PIC algorithm for the analysis of particle cyclotron motion at relativistic speeds, harmonic particle oscillation in the Lorentz-boosted frame, and relativistic Bernstein modes in magnetized charge-neutral (pair) plasmas.

physics.comp-ph↗

arXiv2015

Stable evaluation of Green's functions in cylindrically stratified regions with uniaxial anisotropic layers

We present a robust algorithm for the computation of electromagnetic fields radiated by point sources (Hertzian dipoles) in cylindrically stratified media where each layer may exhibit material properties (permittivity, permeability, and conductivity) with uniaxial anisotropy. Analytical expressions are obtained based on the spectral representation of the tensor Green's function based on cylindrical Bessel and Hankel eigenfunctions, and extended for layered uniaxial media. Due to the poor scaling of these eigenfunctions for extreme arguments and/or orders, direct numerical evaluation of such expressions can produce numerical instability, i.e., underflow, overflow, and/or round-off errors under finite precision arithmetics. To circumvent these problems, we develop a numerically stable formulation through suitable rescaling of various expressions involved in the computational chain, to yield a robust algorithm for all parameter ranges. Numerical results are presented to illustrate the robustness of the formulation including cases of practical interest to geophysical exploration.

math.NA↗

arXiv2013

Stable pseudoanalytical computation of electromagnetic fields from arbitrarily-oriented dipoles in cylindrically stratified media

Computation of electromagnetic fields due to point sources (Hertzian dipoles) in cylindrically stratified media is a classical problem for which analytical expressions of the associated tensor Green's function have been long known. However, under finite-precision arithmetic, direct numerical computations based on the application of such analytical (canonical) expressions invariably lead to underflow and overflow problems related to the poor scaling of the eigenfunctions (cylindrical Bessel and Hankel functions) for extreme arguments and/or high-order, as well as convergence problems related to the numerical integration over the spectral wavenumber and to the truncation of the infinite series over the azimuth mode number. These problems are exacerbated when a disparate range of values is to be considered for the layers' thicknesses and material properties (resistivities, permittivities, and permeabilities), the transverse and longitudinal distances between source and observation points, as well as the source frequency. To overcome these challenges in a systematic fashion, we introduce herein different sets of range-conditioned, modified cylindrical functions (in lieu of standard cylindrical eigenfunctions), each associated with non-overlapped subdomains of (numerical) evaluation to allow for stable computations under any range of physical parameters. In addition adaptively-chosen integration contours are employed in the complex spectral wavenumber plane to ensure convergent numerical integration in all cases. We illustrate the application of the algorithm to problems of geophysical interest involving layer resistivities ranging from 1000 $\Omega \cdot$m to 10$^{-8} \Omega \cdot$m, frequencies of operation ranging from 10 MHz down to the low magnetotelluric range of 0.01 Hz, and for various combinations of layer thicknesses.

math-ph↗