Differential equations lie at the heart of countless scientific and engineering disciplines, modeling a vast array of phenomena, from the evolution of galaxies to the dynamics of financial markets. However, solving these equations, especially in high dimensions or for complex systems, often poses a formidable computational challenge. Quantum computing, with its potential to harness the principles of quantum mechanics for computational advantage, offers a promising avenue for tackling these challenges. Recent years have witnessed remarkable progress in developing quantum algorithms for solving differential equations. These advancements have spurred intense research efforts aimed at unlocking the full potential of quantum computers for simulating complex systems governed by differential equations.

The objective of this thematic program is accelerated progress in developing quantum algorithms for differential equations by bringing numerical analysis and quantum computing experts together, bridging the existing knowledge gap, and initiating meaningful collaborations. Participants will engage in a series of activities designed to promote cross-disciplinary dialogue and advance research. These include week-long workshops that cover both introductory and advanced topics in quantum algorithms and differential equations, brainstorming sessions to identify key challenges, and working groups focused on specific research directions. Weekly seminars and spontaneous seminars will feature presentations from program participants and external experts, fostering knowledge exchange and collaboration.

IMPORTANT:  Registration to the thematic program does not guarantee program participation. You must complete the following form below in addition to Thematic Program Registration (link) to be considered.

Please use the following links if you are:

• Applying for postdoctoral fellowship: Link
• Applying as a long-term (4-6 month) graduate student: Link
• Applying as a visitor to the program: Link

Program Postdoctoral Fellowships

•  Junaid Aftab, University of Maryland
•  Adina Goldberg, University of Waterloo