Project Quantum Computing to solve high energy physics challenges Program DUIRI - Discovery Undergraduate Interdisciplinary Research Internship Term Summer 2024 Status Accepted Research Area Particle Physics and Quantum Computing Description Classical computers use individual bits which store information as binary 0 or 1 states. However, the power of classical computers is becoming saturated which prevents the most complex problems from being solved. Quantum computing offers an exciting solution as quantum bits (called qubits) can exist as a superposition of both 0 or 1. This greatly increases the potential performance of quantum computers as the computing power increases exponentially with the number of qubits. Because of the difficulty in building quantum computers, they are not readily available to the general public. By now multiple platforms exists and that in itself is a challenge to any research field: IBM-Q, D-WAve, Quantinuum, IonQ and others. For example IBM-Q allows anybody to explore tutorials and simulations of the wondrous possibilities of quantum computing. The current proposal seeks undergraduate students to learn how to use, master and compare different platforms for applications in high energy physics - especially towards Quantum Machine Learning and error correction models. This is a complex system and it will take significant time to become prolific. Supervisor Andreas Jung Mentor Andrew James Wildridge Type of work required After learning to use these platforms, the student will apply this knowledge to tackle specific problems in the research area of high-energy physics with applications for reconstructing proton-proton collisions in the Large Hadron Collider. Because learning the basics of these platforms it will take significant time, a follow-up research course in the group or second part could begin at the end of this proposal or in a subsequent DURI.

A particular focus is towards Quantum Machine Learning and error correction models. Websites https://www.physics.purdue.edu/jung/ Qualifications python knowledge is beneficial, physics and some understanding of quantum mechanics or particle physics is an advantage. Credit Hours 3 Weekly Hours 20 (estimated)