SEMINAR 2026
When to Use Quantum Algorithms
| Speaker | Dr Eric R. Anschuetz, Caltech, USA |
| Date/Time | Wednesday, 4 Mar, 3pm |
| Location | S11-02-07 Conference Room |
| Host | Prof Gong Jiangbin |
Abstract
Quantum computers use the principles of quantum mechanics to perform computation in a way fundamentally different from traditional (“classical”) algorithms. This new computational paradigm shows great promise in revolutionizing our ability to understand and solve complex problems in physics, optimization, and learning. However, establishing the precise conditions under which quantum algorithms outperform classical ones is a surprisingly nuanced question. In this talk, I will show how the complexity of typical instances of problems can be understood using ideas from statistical physics, and through this new perspective present a framework for understanding the relative performance of quantum versus classical algorithms in learning and optimization.
Biography
Eric is a Burke Fellow at Caltech who recently completed his PhD at MIT under the joint supervision of Aram Harrow and Misha Lukin. Much of his research involves studying the power of quantum algorithms through the lens of statistical physics and quantum foundations theory. These insights have led to the development of novel quantum algorithms for optimization and learning, and a new, physically-motivated approach to quantum computational complexity.
