Maria Schuld
YOU?
Author Swipe
View article: Alignment and Differentiation: How Language and Network Proximity Drive Opinion‐Based Group Formation Online
Alignment and Differentiation: How Language and Network Proximity Drive Opinion‐Based Group Formation Online Open
This study examines the interplay between language and social connectedness in forming opinion‐based groups on social media. Drawing on small‐world theory and social identity theory, we propose a dual‐layer approach that combines semantic …
View article: The Covid-19 Information Void: How Pro-Vaccination Voices Lost the Narrative in South Africa
The Covid-19 Information Void: How Pro-Vaccination Voices Lost the Narrative in South Africa Open
The erosion of public trust in health information and government communication, particularly during crises like the Covid-19 pandemic, highlights a critical challenge in how health policies are transmitted and received. This study examines…
View article: Polarization on social media: Comparing the dynamics of interaction networks and language‐based opinion distributions
Polarization on social media: Comparing the dynamics of interaction networks and language‐based opinion distributions Open
When people share information and converse on social media, they create “echo chambers” through preferential attachment to like‐minded people and opinions they already support. A great deal of research uses interactional ties between peopl…
View article: A thematic analysis of South African opinions about COVID-19 vaccination on Twitter
A thematic analysis of South African opinions about COVID-19 vaccination on Twitter Open
Vaccine hesitancy is a public health concern in South Africa and internationally. Literature on vaccine hesitancy associates this with mistrust of the government. We present a qualitative analysis of opinions about COVID-19 vaccination exp…
View article: An inductive bias from quantum mechanics: learning order effects with non-commuting measurements
An inductive bias from quantum mechanics: learning order effects with non-commuting measurements Open
There are two major approaches to building good machine learning algorithms: feeding lots of data into large models or picking a model class with an “inductive bias” that suits the structure of the data. When taking the second approach as …
View article: Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data Open
How can we take inspiration from a typical quantum algorithm to design heuristics for machine learning? A common blueprint, used from Deutsch-Josza to Shor's algorithm, is to place labeled information in superposition via an oracle, interf…
View article: Better than classical? The subtle art of benchmarking quantum machine\n learning models
Better than classical? The subtle art of benchmarking quantum machine\n learning models Open
Benchmarking models via classical simulations is one of the main ways to\njudge ideas in quantum machine learning before noise-free hardware is\navailable. However, the huge impact of the experimental design on the results,\nthe small scal…
View article: Generalization despite overfitting in quantum machine learning models
Generalization despite overfitting in quantum machine learning models Open
The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been …
View article: An inductive bias from quantum mechanics: learning order effects with non-commuting measurements
An inductive bias from quantum mechanics: learning order effects with non-commuting measurements Open
There are two major approaches to building good machine learning algorithms: feeding lots of data into large models, or picking a model class with an ''inductive bias'' that suits the structure of the data. When taking the second approach …
View article: Using word embeddings to analyse audience effects and individual differences in parenting Subreddits
Using word embeddings to analyse audience effects and individual differences in parenting Subreddits Open
This paper explores how individuals’ language use in gender-specific groups (“mothers” and “fathers”) compares to their interactions when referred to as “parents.” Language adaptation based on the audience is well-documented, yet large-sca…
View article: Group polarization on social media: Comparing the dynamics of interaction networks and language-based opinion distributions
Group polarization on social media: Comparing the dynamics of interaction networks and language-based opinion distributions Open
Social media has often been blamed for the rising polarization but the research has been restricted to polarized network structures, echo chambers, such as retweet, followership to mentions networks. We use a new measure of opinion languag…
View article: Quantum Computing with Differentiable Quantum Transforms
Quantum Computing with Differentiable Quantum Transforms Open
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant ex…
View article: Using word embeddings to analyse audience effects and individual differences in parenting Subreddits
Using word embeddings to analyse audience effects and individual differences in parenting Subreddits Open
Human beings adapt their language to the audience they interact with. To study the impact of audience and gender in a natural setting, we choose a domain where gender plays a particularly salient role: parenting. We collect posts from the …
View article: Contextuality and inductive bias in quantum machine learning
Contextuality and inductive bias in quantum machine learning Open
Generalisation in machine learning often relies on the ability to encode structures present in data into an inductive bias of the model class. To understand the power of quantum machine learning, it is therefore crucial to identify the typ…
View article: A word embedding trained on South African news data
A word embedding trained on South African news data Open
This article presents results from a study that developed and tested a word embedding trained on a dataset of South African news articles. A word embedding is an algorithm-generated word representation that can be used to analyse the corpu…
View article: Generalization despite overfitting in quantum machine learning models
Generalization despite overfitting in quantum machine learning models Open
The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been …
View article: Using word embeddings to investigate cultural biases
Using word embeddings to investigate cultural biases Open
Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologist…
View article: Is Quantum Advantage the Right Goal for Quantum Machine Learning?
Is Quantum Advantage the Right Goal for Quantum Machine Learning? Open
Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: the machine-learning algorithms of today are notoriously powerful in practice but remain theoretically dif…
View article: Is quantum advantage the right goal for quantum machine learning?
Is quantum advantage the right goal for quantum machine learning? Open
Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficu…
View article: Quantum computing with differentiable quantum transforms
Quantum computing with differentiable quantum transforms Open
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant ex…
View article: Differentiable quantum computational chemistry with PennyLane
Differentiable quantum computational chemistry with PennyLane Open
This work describes the theoretical foundation for all quantum chemistry functionality in PennyLane, a quantum computing software library specializing in quantum differentiable programming. We provide an overview of fundamental concepts in…
View article: Effect of data encoding on the expressive power of variational quantum-machine-learning models
Effect of data encoding on the expressive power of variational quantum-machine-learning models Open
Quantum computers can be used for supervised learning by treating\nparametrised quantum circuits as models that map data inputs to predictions.\nWhile a lot of work has been done to investigate practical implications of this\napproach, man…
View article: Supervised quantum machine learning models are kernel methods
Supervised quantum machine learning models are kernel methods Open
With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circui…
View article: Transfer learning in hybrid classical-quantum neural networks
Transfer learning in hybrid classical-quantum neural networks Open
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid …
View article: Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report
Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report Open
Background The emergence of a novel coronavirus, SARS-CoV-2, in December 2019, progressed to become a world pandemic in a few months and reached South Africa at the beginning of March. To investigate introduction and understand the early t…
View article: Measuring the similarity of graphs with a Gaussian boson sampler
Measuring the similarity of graphs with a Gaussian boson sampler Open
A device called a 'Gaussian Boson Sampler' has initially been proposed as a near-term demonstration of classically intractable quantum computation. As recently shown, it can also be used to decide whether two graphs are isomorphic. Based o…
View article: Circuit-centric quantum classifiers
Circuit-centric quantum classifiers Open
The current generation of quantum computing technologies call for quantum algorithms that require a limited number of qubits and quantum gates, and which are robust against errors. A suitable design approach are variational circuits where …
View article: On quantum ensembles of quantum classifiers
On quantum ensembles of quantum classifiers Open
Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of superposition to store sets of parameters, thereby creating a…
View article: Quantum embeddings for machine learning
Quantum embeddings for machine learning Open
Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a high-dimensional …