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Follow2025 Summer Research Projects
Berkeley Lab’s Computing Sciences summer program offers college undergraduates, graduate students, and faculty opportunities to collaborate with NERSC staff on various science and technology research projects.
NERSC staff start posting projects in January for the upcoming summer. This page will be updated as more projects open and others close. Please contact the project mentor(s) directly to apply for a particular project. Mentors determine who may join a research project, but you must also fill out an application for employment and be in the system before you can be hired. Your application may also be considered for other summer internship opportunities.
Quantum Computing
Evaluating the Performance of Quantum Algorithms for Solving Differential Equations in the NISQ Era and Beyond
Science/CS Domains
Scientific computing: Partial differential equations, Quantum computing
Project Description
In this project, we aim to explore and evaluate the field of quantum algorithms for differential equations. We will build on recent results for near-term variational algorithms [1] and their realization on quantum hardware [2] and study more general theoretical frameworks for solving partial differential equations [3].
Our primary goal is to identify a set of relevant test problems and implement them using both near-term variational algorithms and longer-term scalable quantum algorithms. Secondary project goals could include
- A classical simulation of the algorithms that were implemented using NERSC’s Perlmutter system,
- Proof-of-concept demonstrations of the most promising problems using the IBM Quantum systems available at NERSC, and
- Resource estimation to evaluate the scalability of the identified approaches.
References
[1] Solving nonlinear differential equations with differentiable quantum circuits
[2] Variational quantum algorithms for nonlinear problems
[3] Quantum simulation of partial differential equations via Schrodingerisation: technical details
Desired Skills/Background
- Background in scientific computing and familiarity with partial differential equations
- Solid foundation in quantum computing
- Experience with Python
- Nice to have: Experience with quantum hardware runs
Mentor(s)
Daan Camps (dcamps@lbl.gov), Jan Balewski (balewski@lbl.gov)
Development and Testing of a Quantum Protocol for Polynomial Transformations of Data Sequences using Quantum Signal Processing
Science/CS Domain(s)
Quantum signal processing, data encoding
Project Description
Purpose
Develop a protocol that computes polynomial transformations on data sequences and test that protocol on a shot-based simulator.
Method
Implement Python code to develop and test the protocol using a shot-based circuit simulator in Qiskit
Overview
Recent advancements in efficient data encoding into quantum states have enabled quantum-based operations on such data. Notably, the QCrank encoder [1] facilitates the input of sequences of real values into quantum processing units (QPUs). Additionally, quantum signal processing (QSP) techniques [2] provide a practical framework for performing computations on scalar polynomials.
The goal of this internship is to integrate these two approaches and develop the mathematical foundations for a protocol that computes low-degree polynomials on sequences of dozens of real numbers.
This protocol will be implemented in the Qiskit shot-based simulator and tested on NERSC systems. Existing implementations of QCrank [3], QSPpack [4] and pyQSP [5] will serve as foundational resources.
Stretch goals for this project include developing extensions to multivariate polynomials and improving the encoding schemes to take further advantage of data sparsity.
The intern will have the opportunity to contribute significantly to this cutting-edge field with the potential to co-author a publication upon successful implementation.
References
[1] QCrank protocol
[2] QSP theory
[3] QCrank-light reference code
[4] QSPpack reference code
[5] pyQSP reference code
Desired Skills/Background
- Understanding of the mathematical foundation of quantum computations
- Experience in Python, Mathematica, and Qiskit
- Familiarity with HPC environments and containers
Mentors
Jan Balewski (balewski@lbl.gov), Daan Camps (dcamps@lbl.gov)
Application Performance
Data/Machine Learning
Infrastructure
Power/Energy Efficiency
