Click any of the Quantum Science proposal summaries below for more information and to apply as a Junior Researcher.
Your deadline to apply is June 16.
Return to the complete list of available research opportunities.
GPU speedup for an exploration of non-Markovianity and qubit systems (Shandera)
The purpose of this project is to update a code for quantum circuits, developed in Professor Sarah Shandera’s group, to run on GPUs. (Learn more and apply)
Machine-Learning Angle-Resolved Photoemission Spectroscopy under Tunable Magnetic Fields (Chaoxing Liu)
This project closely aligns with the objectives of CENSAI by leveraging machine learning approach to guide the design of cutting-edge experiments and drive foundational advances in quantum materials research. (Learn more and apply)
Quantum Algorithms and Quantum Enhanced Machine Learning for Transient Simulations in Large Scale Nonlinear Dynamical Systems (Xiantao Li, Yan Li)
By reformulating classical numerical algorithms as quantum workflows and benchmarking them on stateoftheart IBM, QuEra, and IQM hardware, we leverage the very “advanced computational and datascience approaches” ICDS seeks to promote. (Learn more and apply)
Unrestricted MeanField Analysis for Quantum Materials via Machine Learning (Zhen Bi)
By combining quantum materials expertise with machine learning tools, this project will produce an opensource package that automates and speeds unrestricted meanfield calculations for interacting electronic systems. (Learn more and apply)
Prediction and Discovery of Many-Body Phenomena in Quantum Simulators Enabled by High-Performance Computation (Bryce Gadway)
This project is aimed at developing tight-knit collaborations between theory and experiment for the development of quantum simulations and quantum computation at Penn State related to Rydberg quantum simulator experiments. (Learn more and apply)
Quantum simulations of many body physics and field theories (Ribhu Kaul)
In this project we will study such lattice models on classical supercomputers to learn their basic phase diagrams and identify which of these lattice models provide the most efficient description of the quantum field theories of interest, paving the way for an eventual quantum simulation. (Learn more and apply)
Benchmarking for Quantum Machine Learning (Mahmut Taylan Kandemir)
The goal of QML benchmarking is to establish a rigorous, standardized, and practical (easy to use) framework for systematically evaluating and comparing QML systems—spanning algorithms, hardware systems, and application domains. (Learn more and apply)
Exploring Phonon Dynamics of Defective Transition Metal Dichalcogenides (TMDs) using Quantum Mechanics, ReaxFF and Machine Learning Methods (Adri van Duin)
Our long term goal is to combine reactive MD simulations with machine learning techniques for exploring the distributions of defective systems in experimental measurements, such as Raman spectra and high-resolution transmission electron microscopy to advance the understand of phonon dynamics and structural evolutions of defective TMD systems and pave the way for their applications in nanodevices. (Learn more and apply)
Computational Perspectives for Quantum Phases of Matter (Zhen Bi)
By analyzing how the mixing time scales with system size, temperature, and Hamiltonian parameters, this project will develop a computational perspective for comparing quantum phases and for pinpointing the dynamical signatures of phase transitions. (Learn more and apply)
Neural-Network based optimization of wave functions of interacting electrons (Jainendra Jain)
In this project, we will initialize our model on CF trial wavefunctions and fine-tune to capture only the residual LL mixing effects—a transfer-learning strategy that slashes parameter requirements and opens the door to much larger systems. (Learn more and apply)