Colloquium – Eric Anschuetz

Eric Anschuetz, Massachuesetts Institute of Technology

When to Go QuantUM

Quantum computers use the principles of quantum mechanics to perform computation in a way fundamentally different from traditional (“classical”) methods. This new computing paradigm exhibits great promise in–among other things–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 argue that statistical physics offers the ideal lens for resolving this open question. I will present a framework for predicting the relative performance of quantum versus classical algorithms in optimization and simulation, and demonstrate how these same ideas can be used to reveal new phases of matter in quantum many-body systems.

Bio: Dr. Eric Anschuetz 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 limitations 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.

February 11, 2026 @ 1:00pm (CST) in Commons Center 237

Host: Kalman Varga