The Senior Project is an independent, student-led culmination of our high school experience. After three years of academic preparation, our seniors are ready to spend the last trimester of their high school careers applying the skills and knowledge they have gained to develop a project that is insightful, academically rigorous, and professional in nature.

Our seniors start by designing a research question that is often centered on a subject they are passionate or curious about. Then they embark on a journey to answer it, documenting and analyzing their findings as they go. They partner with both an internal and external advisor to support and guide their research. Students may choose to conduct their research in the form of internships or experimental research at university research labs, field work abroad, or research conducted remotely from home. From explorations into new-age technology to cutting-edge medical advancements to social justice, the Senior Project offers students the opportunity to channel their innate curiosity. This experience readies them for the type of self-direction and self-discipline expected in an undergraduate and graduate setting.

This year, we are proud to showcase a senior from one of our neighboring campuses, BASIS Independent Fremont, Patrick Z.

Project Title: Comparative Analysis of Error Mitigation for Quantum Systems and Artificial Neural Networks Under Noisy Inputs

BASIS Independent Advisor: Ms. Shahin

Internship Location: Google, 1600 Amphitheatre Pkwy, Mountain View, CA 94043

Onsite Mentor: Mr. Peng Xiang Li

Abstract: Artificial Intelligence is increasingly vital to high-stakes industries like healthcare and finance, yet these systems often falter when processing the noisy, imperfect data found in real-world environments. While Convolutional Neural Networks (CNNs) are the standard for image classification, Quantum Neural Networks (QNNs) theoretically offer superior noise resilience through quantum properties such as superposition and entanglement. However, existing comparative research is often inconsistent, frequently pitting high-resolution classical models against hardware-limited, low-resolution quantum simulations. This research addresses that disparity by conducting a comparative analysis of error mitigation between QNNs and CNNs under Additive White Gaussian Noise (AWGN). My methodology involves developing three distinct models within TensorFlow Quantum using the MNIST dataset: a standard full-resolution CNN, a hybrid QNN, and a resolution-matched fair CNN to ensure a direct architectural comparison. All models will be trained on clean data and subsequently tested against noise-injected datasets to measure classification accuracy and loss. Through this research, I aim to quantify whether quantum architectures inherently outperform classical ones in signal-to-noise processing. Observing superior robustness in the QNN would validate the practical viability of quantum machine learning for critical applications where data integrity is compromised.

BASIS Independent Dublin is a Grades 6 – 12 private school, providing students with an internationally benchmarked liberal arts and sciences curriculum, with advanced STEM offerings. Considering joining the BASIS Independent Dublin community? To join our interest list for the next school year and receive admissions updates and more, please click here.