The Workshop on Computational Complexity and High Energy Physics was held July 31, 2017 through August 2, 2017 at the University of Maryland by the Joint Center for Quantum Information and Computer Science.
For more information, visit the CC&HEP 2017 website at http://cchep2017.quics.umd.edu/.
Over the past few years a number of interesting connections have emerged at the interface between computational complexity theory and high energy physics. This workshop will bring together experts in this emerging field. Some of the connections that we hope to explore at this workshop include:
- It has been conjectured that quantum computers can efficiently simulate all dynamical processes in nature. This is a bold claim at the root of both computational complexity and physics. Is it true? The strongest challenges to this conjecture come from high energy physics.
- The theory of quantum error correcting codes has begun to inform our understanding of gauge/gravity duality. Conversely, the study of this duality has led to the study of new quantum error correcting codes called holographic codes, which could find application in quantum technologies.
- Recently, based on evidence from gauge/gravity duality, it has been conjectured that the complexity of a quantum state of certain field theories, as measured by the number of quantum gates needed to produce them, is equal to the Einstein-Hilbert action of the spacetime to which the state is dual.
- The black hole information paradox is at the heart of the problem of formulating a complete theory of quantum gravity. Recently, it has been proposed that the computational infeasibility of decoding Hawking radiation may make it infeasible for some of the troublingly paradoxical predictions to be observed by computationally bounded observers.
- Tensor networks have been highly successful in condensed matter physics as both purely theoretical tools and as the basis for numerical methods. Recently, efforts have begun to apply tensor networks to problems in high energy physics.
- Computationally difficult problems can arise in many areas of high energy physics, ranging from predicting experimental outcomes in particle accelerators to finding physically realistic solutions in a landscape of possible vacua. In many cases, modern algorithms and computational complexity may shed light on these problems, establishing either their intractability or yielding efficient procedures for solving them.