Following her passion for advancing educational methodologies in the emerging field of quantum machine learning (QML), Nicola is dedicated to bridging the gap between classical machine learning (ML) and quantum computing education. Her goal is to conduct impactful research, collaborate with leading experts, and develop effective educational pathways to prepare the next generation of quantum computing professionals.

As an experienced Computer Science subject teaching lead, she holds a Bachelor of Education and an MSc in Computer Science with Distinction, providing a strong foundation in ML and computational theory.

Transitioning from an initial career in the creative industries, she has over 20 years’ experience in the education sector, encompassing work and additional study in the UK, USA, Japan, and Australia. This includes advanced training in developing AI applications from the Oxford University Department for Continuing Education, and space-related STEM curriculum engagement via the NASA STEM EPDC cooperative between NASA and Texas State University’s LBJ Institute.

During past tenure at the Girls' Day School Trust (GDST) in the UK, she wrote and launched an advanced A-Level diploma curriculum focused on remote sensor AI, design optimisation and ML programming for space technology, supported by grant funding from the Royal Society STEM Partnership and the AWS Proof of Concept Program. Incorporating an introductory module in quantum computing and QML, Nicola developed the curriculum in collaboration with specialists from NASA Earth Science Data Systems and the UK Astronomy Technology Centre. She was recognised in Education Today magazine and as a finalist for the TES Awards 2024 (Best Use of Technology), ISOY Awards 2023 (Outstanding New Initiative) and Women of the Future Awards 2022 (Mentor of the Year) for rolling out the program to GDST girls' schools nationally.

Her more recent endeavours include delivering coding masterclasses in ML, QML, and design optimisation at the UKSEDS Conference and Staffordshire University London, co-presenting curriculum innovations at EDUtech Europe, and serving as a Technical Innovation Specialist Mentor for the BU Spark! learning lab at Boston University Faculty of Computing & Data Science. These roles have honed her mentorship and curriculum project management skills, essential for conducting impactful research. Through her work, she aims to drive progress in technology education, addressing the critical gap in translating classical ML processes for quantum systems.

By championing innovative pedagogical approaches, she strives to empower students to become active creators in the field of quantum computing, setting a precedent for interdisciplinary and emerging technology education.

Research interests

• Quantum Machine Learning (QML) and Quantum Computing Algorithms
• Classical Machine Learning and Data Augmentation
• AI Applications in Space Technology
• Educational Methodologies, Curriculum Design and Instructional Strategies in Computer Science
• Mentorship and Project Management in STEM Education

Thesis

Optimising Skill Transition Pathways from Classical to Quantum Machine Learning

Quantum Machine Learning (QML) merges quantum computing with classical machine learning (ML), promising advancements in AI, data analysis, and cybersecurity. As quantum technology evolves, QML is set to optimise quantum systems and foster innovation. However, translating traditional ML processes to quantum systems remains challenging due to QML's complexity. This research aims to create an educational framework to bridge this gap. By leveraging foundational ML knowledge, the curriculum will introduce QML concepts from quantum state preparation to quantum circuits. Hands-on learning with quantum computing platform interface tools will allow learners to explore real-world quantum applications. Using a Design-Based Research (DBR) methodology, the study will track learner progress and refine teaching strategies through iterative feedback from learners and industry experts. The project focuses on refining QML coding methods to mirror classical ML processes on quantum computers. Key phases include analysing learning barriers, developing curriculum materials, piloting lessons, and evaluating effectiveness. Emphasising active, experiential learning, the research aims to boost learners' confidence and understanding of quantum paradigms. The expected outcome is a validated educational framework for QML, offering strategies to address the global skills gap in quantum technologies and preparing a skilled workforce for future quantum-enabled industries.

Principal supervisor: Mary Webb

Secondary supervisor: Peter Kemp