#### People

# Seth Lloyd

## Fellow - 2013

### M.I.T.

Fields of study:

Theoretical Physics

Seth Lloyd is Professor of Mechanical Engineering at MIT. Prof. Lloyd is interested in information and the role it plays in physical systems, particularly systems in the quantum regime. He has worked on the definition of complexity using insights gained from the work of Rolf Landauer on physics of information processing and of Charles Bennett on computational measures of complexity. The definition of complexity he developed is related to the amount of thermodynamic resources required to perform a given task. Subsequently, he became a collector of measures of complexity: despite the proliferation of apparently disparate methods for quantifying the complex, he has been able to show that most such measures are closely related to each other. The definitions measure either information --- the difficulty of describing a thing; or effort --- the difficulty of accomplishing a task; or both. (Please send your measures of complexity to Seth at slloyd@mit.edu. In return, he will send you his current list.) The realization that information processing and physics are intimately related lie at the heart of Prof. Lloyd's current work on quantum computation. Quantum computers are in essence complex quantum systems. In collaboration with Murray Gell-Mann he is working on a theory of quantum complexity. He hopes to use quantum mechanical computers to test methods by which the highly detailed, complex world that we see around us arose out of a highly symmetric, simple quantum world in the early universe. As part of learning his current job as a professor of Mechanical Engineering at MIT, he has discovered the importance of work on complex systems for society as a whole. When the operation of a new car relies on the proper functioning of twenty microprocessors, when fear of a computer glitch inspires a global apocalyptic movement, when electronic devices from VCRs to toasters act as if they had minds of their own, it's clear that our future welfare depends crucially on understanding how complex systems get information, and what they do with it.