quantum computing, machine learning, programming, data analysis
WHAT WE DO
QML research stream is the first course at UMD to introduce first year students to a burgeoning new space that is currently developing at the intersection of two fascinating fields: Quantum Computing and Machine Learning.
Quantum Machine Learning (QML) is a research stream started in 2022 as a part of the FIRE program at the University of Maryland, College Park under the Technology & Applied Science cluster. Since 2023 we are in collaboration with the National Quantum Laboratory at Maryland (QLab) and one of the world's leading quantum computing hardware and software companies IonQ.
Several existing courses at UMD cover the distinct fields of machine learning (ML) and quantum computing (QC) independently, but the QML research stream is the first one at the university to introduce first year students to a burgeoning new space that is currently developing at the intersection of these fields: quantum machine learning.
The QML stream offers students a unique opportunity to gain early access and in-depth insights into this captivating new technology. It provides a hands-on exploration, both at the software and hardware levels, of one of today's most intriguing and rapidly evolving fields of science. This experience places you ahead of the curve and allows you to engage directly with the forefront of innovation.
WHY IT MATTERS
Quantum computing represents the future of computing technology. Understanding its principles and potential applications equips students with the knowledge and skills needed to be at the forefront of technological advancements.
Quantum computing matters because it has the potential to revolutionize various fields, accelerate scientific discovery, address complex global challenges, and usher in a new era of technology and innovation. As quantum hardware and algorithms continue to advance, the practical applications and benefits of quantum computing are becoming increasingly evident.
This course provides an opportunity to design, create, run and deploy quantum circuits and algorithms to solve complex tasks. This is a new and rare skill set that will certainly be in-demand as Quantum Computing and Quantum Machine Learning matures.
WHAT YOU LEARN
Focus on foundational concepts in quantum computing and machine learning.
To explore the accomplishments of current and past QML researchers, please visit the class website: https://qmlfire.github.io/
If you feel you are not ready to commit to two semesters of QML at this stage, but instead would just like to explore the field a bit, I'd be delighted to assist and support you in your exploration. The QML website already provides access to a number of resources, including tutorials and a library of lecture videos, which can serve as a starting point for your journey. Feel free to reach out whenever you need guidance or have questions—I'm here to help facilitate your exploration of QML.
QML is a year-long, comprehensive research experience.
Students will spend their initial weeks creating strong foundational understanding of fundamental concepts in quantum computing and machine learning, including Superposition, Entanglement, Quantum Gates, Quantum Algorithms, Quantum Parallelism, Quantum Decoherence, Quantum Supremacy, and principles of artificial intelligence, deep learning, classification/regression problems. Regarding quantum hardware, our primary focus centers on Trapped Ions and Superconducting Qubits, as well as the critical domain of Quantum Error Correction. Rest of the semester we will work on google Colab notebooks and study speculated impacts of QC over classical computers in fields such as cryptography, optimization, and machine learning data analysis in general.
Now our emphasis shifts towards conceiving and executing independent projects. These projects can be quantum computing software or data analysis-centric, or they can revolve around quantum hardware. We use the concepts and the state-of-the-art tools and techniques in quantum computing and machine learning to analyze Big Data sourced directly either from detectors recording high-energy proton-proton collisions at the Large Hadron Collider (LHC) at CERN in Geneva or particle data collected by underground experiments, originating from astrophysical sources such as supernovas, gamma ray bursts, and black holes.
On the hardware side, we will characterize IonQ quantum hardware, including error rates, gate fidelities, noise characteristics, and optimize quantum algorithms to achieve quantum advantage using IonQ hardware.
Dr. Shabnam Jabeen
FIRE Faculty Leader
Dr. Sarah Eno
Dr. Alberto Belloni