arXiv subjects

Jacob Sherson

Publications and source records attributed to Jacob Sherson.

At least 19 recordsLinked to original sources

arXiv2026

National Quantum Strategies: A Data-Driven Approach to Understanding the Quantum Ecosystem

As quantum technologies (QT) move from foundational research toward industrial and societal deployment, national strategies have become critical instruments for shaping the future of this emerging field. In this study, we conduct the first large-scale, data-driven analysis of 62 national quantum strategic documents (QSDs) from 20 countries. Using AI-based natural language processing (topic modeling), we identify 12 topics present in the text, ranging from technical development areas to transversal aspects such as workforce development and governance. Temporal analysis reveals a distinct shift in policy discourse toward applications of QT and commercialisation, and relatively away from basic science. Our findings highlight the increasing diversification of the QT field, and contribute to the growing area of quantum policy studies. We advocate for more AI and data-driven analyses of the quantum ecosystem, to work toward a scalable framework for understanding the technological and societal challenges of the second quantum revolution.

physics.soc-ph↗

arXiv2025

The Quantum Technology Job Market: Data Driven Analysis of 3641 Job Posts

The rapid advancement of Quantum Technology (QT) has created a growing demand for a specialized workforce, spanning across academia and industry. This study presents a quantitative analysis of the QT job market by systematically extracting and classifying 3641 job postings worldwide. The classification pipeline leverages large language models (LLMs) whilst incorporating a "human-in-the-loop" validation process to ensure reliability. The research identifies key trends in regional job distribution, degree and skill requirements, and the evolving demand for QT-related roles. Findings reveal a strong presence of the QT job market in the United States and Europe, with increasing corporate demand for engineers, software developers, and PhD-level researchers. Despite growing industry applications, the sector remains in its early stages, dominated by large technology firms. However, there are signs that change may be forthcoming, indicated by a strong presence of Bachelor's and Master's graduates in the workforce.

physics.ed-ph↗

arXiv2025

From Interaction to Collaboration: How Hybrid Intelligence Enhances Chatbot Feedback

Generative AI (GenAI) chatbots are becoming increasingly integrated into virtual assistant technologies, yet their success hinges on the ability to gather meaningful user feedback to improve interaction quality, system outcomes, and overall user acceptance. Successful chatbot interactions can enable organizations to build long-term relationships with their customers and users, supporting customer loyalty and furthering the organization's goals. This study explores the impact of two distinct narratives and feedback collection mechanisms on user engagement and feedback behavior: a standard AI-focused interaction versus a hybrid intelligence (HI) framed interaction. Initial findings indicate that while small-scale survey measures allowed for no significant differences in user willingness to leave feedback, use the system, or trust the system, participants exposed to the HI narrative statistically significantly provided more detailed feedback. These initial findings offer insights into designing effective feedback systems for GenAI virtual assistants, balancing user effort with system improvement potential.

cs.HC↗

arXiv2024

Quantum Technology masters: A shortcut to the quantum industry?

In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of masters programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 masters programs, with substantial growth since 2021. Over time masters have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore masters programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, quantum program enhancements, which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of masters programs as a route to the quantum industry.

physics.ed-ph↗

arXiv2024

Facilitating Human Feedback for GenAI Prompt Optimization

This study investigates the optimization of Generative AI (GenAI) systems through human feedback, focusing on how varying feedback mechanisms influence the quality of GenAI outputs. We devised a Human-AI training loop where 32 students, divided into two groups, evaluated AI-generated responses based on a single prompt. One group assessed a single output, while the other compared two outputs. Preliminary results from this small-scale experiment suggest that comparative feedback might encourage more nuanced evaluations, highlighting the potential for improved human-AI collaboration in prompt optimization. Future research with larger samples is recommended to validate these findings and further explore effective feedback strategies for GenAI systems.

cs.HC↗

arXiv2023

A Framework for Curriculum Transformation in Quantum Information Science and Technology Education

The field of Quantum Information Science & Technology (QIST) is booming. Due to this, many new educational courses and university programs are needed in order to prepare a workforce for the developing industry. Owing to its specialist nature, teaching approaches in this field can easily become disconnected from the substantial degree of science education research which aims to support the best approaches to teaching in Science, Technology, Engineering & Mathematics (STEM) fields. In order to connect these two communities with a pragmatic and repeatable methodology, we have synthesised this educational research into a decision-tree based theoretical model for the transformation of QIST curricula, intended to provide a didactical perspective for practitioners. The Quantum Curriculum Transformation Framework (QCTF) consists of four steps: 1. choose a topic, 2. choose one or more targeted skills, 3. choose a learning goal and 4. choose a teaching approach that achieves this goal. We show how this can be done using an example curriculum and more specifically quantum teleportation as a basic concept of quantum communication within this curriculum. By approaching curriculum creation and transformation in this way, educational goals and outcomes are more clearly defined which is in the interest of the individual and the industry alike. The framework is intended to structure the narrative of QIST teaching, and with future testing and refinement it will form a basis for further research in the didactics of QIST.

physics.ed-ph↗

arXiv2023

Contributions from Pilot Projects in Quantum Technology Education as Support Action to Quantum Flagship

The GIREP community on teaching and learning quantum physics and the Education section of the Quantum flagship project of the European Union (QTEdu) have brought together different stakeholders in the field of teaching quantum physics on all levels, including outreach. The goal of QTEdu is to pave the way for the training of the future quantum workforce. To this end, it is necessary to understand the needs of the quantum technology (QT) field, make the general public aware of the existence and importance of QT, and introduce quantum physics already in high school, so that high school students can choose QT as their field of study and career. Finally, new university courses need to be established to support emerging specific profiles such as a quantum engineer. In this symposium, four QTEdu pilot projects were brought together to demonstrate how their complementary approaches have worked towards realising the above goals.

physics.ed-ph↗

arXiv2022

Modelling assisted tunneling on the Bloch sphere using the Quantum Composer

The Bloch sphere representation is a geometric model for all possible quantum states of a two-level system that can be used to describe the time dynamics of a qubit. As explicit application, we consider the time dynamics of a particle in a double-well potential. In particular, we adopt a recent method for off-resonant excitations, the so-called SUPER principle (Swing-UP of the quantum emitter population) driven by periodic electromagnetic fields, to the context of quantum tunnelling. We show that the tunnelling probability can be enhanced significantly when an appropriate oscillation of the potential height is introduced. Driven by a collaborative approach we call educator-developer dialogue, an updated version of the software Quantum Composer is presented. For educational purposes, we map the two lowest energy states of the 1D-Schr\"odinger equation to the Bloch sphere representation, leading to a rather clear and intuitive physical picture for the pertinent time dynamics.

physics.ed-ph↗

arXiv2021

Predicting quantum dynamical cost landscapes with deep learning

State-of-the-art quantum algorithms routinely tune dynamically parametrized cost functionals for combinatorics, machine learning, equation-solving, or energy minimization. However, large search complexity often demands many (noisy) quantum measurements, leading to the increasing use of classical probability models to estimate which areas in the cost functional landscape are of highest interest. Introducing deep learning based modelling of the landscape, we demonstrate an order of magnitude increases in accuracy and speed over state-of-the-art Bayesian methods. Moreover, once trained the deep neural network enables the extraction of information at a much faster rate than conventional numerical simulation. This allows for on-the-fly experimental optimizations and detailed classification of complexity and navigability throughout the phase diagram of the landscape.

quant-ph↗

arXiv2021

Revisiting Citizen Science Through the Lens of Hybrid Intelligence

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss the benefits of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Currently there is no clear framework or methodology on how to create such an effective mixture. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of HI and human-centered AI of the 21st century, while benefiting CS as well. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This "HI lens" provides the CS community with an overview of several ways to utilize the combination of AI and human intelligence in their projects. It also allows the AI community to gain ideas on how developing AI in CS projects can further their own field.

cs.HC↗

arXiv2021

SciNote: Collaborative Problem Solving and Argumentation Tool

As educators push for students to learn science by doing science, there is a need for computational scaffolding to assist students' evaluation of scientific evidence and argument building. In this paper, we present a pilot study of SciNote, a CSCL tool allowing educators to integrate third-party software into a flexible and collaborative workspace. We explore how SciNote enables teams to build data-driven arguments during inquiry-based learning activities.

cs.CY↗

arXiv2020

The power of pictures: using ML assisted image generation to engage the crowd in complex socioscientific problems

Human-computer image generation using Generative Adversarial Networks (GANs) is becoming a well-established methodology for casual entertainment and open artistic exploration. Here, we take the interaction a step further by weaving in carefully structured design elements to transform the activity of ML-assisted imaged generation into a catalyst for large-scale popular dialogue on complex socioscientific problems such as the United Nations Sustainable Development Goals (SDGs) and as a gateway for public participation in research.

cs.HC↗

arXiv2020

crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players "blend" existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.

cs.HC↗

arXiv2020

Hessian-based optimization of constrained quantum control

Efficient optimization of quantum systems is a necessity for reaching fault tolerant thresholds. A standard tool for optimizing simulated quantum dynamics is the gradient-based \textsc{grape} algorithm, which has been successfully applied in a wide range of different branches of quantum physics. In this work, we derive and implement exact $2^{\mathrm{nd}}$ order analytical derivatives of the coherent dynamics and find improvements compared to the standard of optimizing with the approximate $2^{\mathrm{nd}}$ order \textsc{bfgs}. We demonstrate performance improvements for both the best and average errors of constrained unitary gate synthesis on a circuit-\textsc{qed} system over a broad range of different gate durations.

quant-ph↗

arXiv2019

Optimization of Pulses with Low Bandwidth for Improved Excitation of Multiple-Quantum Coherences in NMR of Quadrupolar Nuclei

We discuss the commonly encountered problem when optimizing NMR pulses using optimal control that the otherwise very precise NMR theory does not provide as excellent agreement with experiments. We hypothesize that this disagreement is due to phase transients in the pulse due to abrupt phase- and amplitude changes resulting in a large bandwidth. We apply the GROUP algorithm that gives high fidelity pulses with a low bandwidth compared to the typical GRAPE pulses. Our results obtain a better agreement between experiment and simulations supporting our hypothesis and solution to the problem.

quant-ph↗

arXiv2019

(Of course) algorithms beat gamers at solving quantum control problems

Dries Sels and Allan Gr{\o}nlund have demonstrated numerical algorithms that outperform the players of our Quantum Moves game and they call the conclusions of our article Exploring the Quantum Speed Limit with Computer Games, Nature, 532, 210 (2016)] 'untenable'. Sels and Gr{\o}nlund ignore that our article already emphasizes that the players do not solve the quantum optimization challenge to any desired accuracy. Our key finding is that player solutions can be employed either as seeds for numerical optimization algorithms or as guidance towards low-dimensional representations of the solutions.

quant-ph↗

arXiv2019

Geometry of turbulent dissipation and the Navier-Stokes regularity problem

The question of whether a singularity can form in an initially regular flow, described by the 3D incompressible Navier-Stokes (NS) equations, is a fundamental problem in mathematical physics. The NS regularity problem is super-critical, i.e., there is a 'scaling gap' between what can be established by mathematical analysis and what is needed to rule out a singularity. A recently introduced mathematical framework--based on a suitably defined `scale of sparseness' of the regions of intense vorticity--brought the first scaling reduction of the NS super-criticality since the 1960s. Here, we put this framework to the first numerical test using a spatially highly resolved computational simulation performed near a 'burst' of the vorticity magnitude. The results confirm that the scale is well suited to detect the onset of dissipation and provide strong numerical evidence that ongoing mathematical efforts may succeed in closing the scaling gap.

math-ph↗

arXiv2019

Global optimization of quantum dynamics with AlphaZero deep exploration

While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics.

quant-ph↗