Quantum Information for
Mathematics, Economics, and Statistics
May 24-28, 2021
- Scott Aaronson, (Computer Science, University of Texas at Austin)
- David Awschalom (Prizker School of Molecular Engineering, Chicago)
- Brian DeMarco (Physics, UIUC)
- Marius Junge (Mathematics, UIUC)
- Paul Kwiat (Physics, UIUC)
- Umesh Vazirani (EECS, Berkeley)
There are many practical and theoretical challenges in the emerging area of quantum information and computing, which seeks to make effective use of the information embedded in the state of a quantum system, and promises to solve previously intractable computational problems and revolutionize simulation. The engineering ambition is two-fold. The first is to come up with computational hardware that sidesteps the physical limits on computational power of existing computer technologies, which are ultimately constrained by limits on the energy dissipated as the physical size of the building blocks of computational circuitry approaches a few nanometers. The second is the construction of ultrasensitive devices to detect biological and chemical changes. In parallel with the practical difficulties, new theory is required to understand the advantages and limitations of quantum media. This includes the developments of quantum cryptography promising “unhackable” communications, new quantum algorithms with the promise to solve previously intractable computational problems and to revolutionize simulation, remote verification, and the study of random circuits for benchmarking. At the moment, quantum information theory draws inspiration from many different aspects of mathematics.
Registration will open soon.