Job Opportunities in HEP.QPR

News: We currently have an opening for a postdoctoral scholar to join us. Please take a look at our posting here: https://academicjobsonline.org/ajo/jobs/13801 and apply via ajo if you're interested.

We have openings for graduate students and undergraduate students for summer 2019 and fall 2019. See below for some example projects. Please get in contact with us if you're interested!

Grad Student Research Projects

Project 1: Pattern recognition algorithms for quantum computers

Charged particles produced in the proton-proton collisions at the LHC are reconstructed using pattern recognition algorithms. These algorithms play a key role in event reconstruction but require large amounts of computing power. Track formation is the pattern recognition step that combines 3D measurements of each charged particle position as it traverses detector layers into a particle track candidate. One approach which has worked for past HEP experiments is to express the problem as a Quadratic Unconstrained Binary Optimization [STIMPFL]. Due to the complexity of the resulting energy function, stochastic annealing may allow finding a global minimum faster and more accurately than other numerical methods, and quantum annealing should perform better still. In this project, we will formulate a suitable QUBO problem, and compare the performance of simulated stochastic annealing optimization running on classical computers with quantum annealing running on D-wave Systems quantum annealers, and on universal quantum computers. This project would be performed as part of the HEP.QPR project. The ideal candidate for this project would have a background in computing. Familiarity with pattern recognition algorithms and/or quantum computing would be a plus. No background in particle physics is required. The supervisor for the project would be Professor Heather Gray.

Project 2: Particle track ambiguity resolution algorithms for quantum computers

Charged particles produced in the proton-proton collisions at the LHC are reconstructed using pattern recognition algorithms. These algorithms play a key role in event reconstruction, but require large amounts of computing power. Ambiguity resolution is the algorithmic step in pattern recognition which studies the different track candidates and selects the highest quality ones to become the final reconstructed tracks. We plan to explore the potential for quantum computers to be used for track ambiguity resolution, which could address the limitation of current classical algorithms which process tracks sequentially. If so, this could significantly improve the algorithmic performance and, in the future, reduce the computational needs In this project, we would focus on developing such an algorithm on track candidates within jet cores. In the initial stages of the project, the student would focus on the theoretical development of such an algorithm for a quantum computer, which, if successful, would later be tested either on a simulated or real quantum computer. This project would be performed as part of the HEP.QPR project. The ideal candidate for this project would have a background in computing and/or computational theory. No background in particle physics is required. The supervisor for the project would be Professor. Heather Gray.