Summer School Topic: Fault-tolerant Algorithms in Quantum Computing

Dates: July 27 to August 7, 2026

Location: 103 Gross Hall, Duke University, Durham, North Carolina

Application is now open: Link ! Application Deadline: March 1st 2026.

The program consists of two weeks. Week 1 will cover quantum fundamentals and core quantum computing techniques (no prior background in quantum computing will be assumed). Week 2 will focus on a range of advanced research topics. We will also include a lab tour of the Duke Quantum Center, where participants will have the opportunity to see quantum hardware in person.

Organizing Committee:

• Robert Calderbank (Charles S. Sydnor Distinguished Professor@ CS, Math, ECE)
• Di Fang (Assistant Professor@ Math; committee chair)
• Jianfeng Lu (James B. Duke Distinguished Professor@ Math)
• Yu Tong (Assistant Professor@ Math, ECE)
• Hongkai Zhao (Ruth F. DeVarney Distinguished Professo@ Math)
  
  

The events are locally hosted and administratively supported by:
Department of Mathematics (primary host; Chair: Hongkai Zhao)
Rhodes Information Initiative at Duke (Director: Jonathan Mattingly)
Duke Quantum Center (Director: Christopher Monroe)

If you have any questions for the organizing committee, please email the committee chair.

Overview:

Quantum computing stands at the forefront of scientific innovation, captivating broad interest as a dynamically evolving field. Recent years have witnessed remarkable progress in developing and analyzing quantum computing algorithms (more commonly known as quantum algorithms) for a wide range of scientific computing challenges. These applications include various numerical linear algebra tasks, solving high-dimensional differential equations, learning from quantum systems, and more.

In this summer school, we will introduce quantum algorithms and quantum computing from a numerical analysis and applied mathematics perspective. We will first go over fundamental principles and basics of quantum mechanics and quantum computing, and then delve into discussing quantum algorithms of various tasks for scientific computing purposes, including quantum dynamics simulation, numerical linear algebra tasks, numerical differential equation, and quantum learning tasks.

During the first week, participants will systematically acquire the mathematical foundations of quantum algorithms and quantum computing. The program will introduce contemporary and essential techniques for constructing quantum algorithms, including quantum phase estimation, Trotterization, Linear Combination of Unitaries (LCU), block-encoding, quantum signal processing (QSP), and quantum singular value transformation (QSVT). In the second week, we will delve into more advanced topics, such as quantum dynamics simulation, quantum advantage, quantum learning theory, etc. No prior knowledge of quantum physics or quantum computing will be assumed.