Abstract

Stochastic processes are ubiquitous in our daily lives. Financial markets, the weather, genetic mutation, earth quake patterns — all are governed by statistical uncertainty as well as structured behaviour. Complexity science has long taken an interest in such phenomena and asked: What is it that makes a stochastic process complex? Can we meaningfully compare the complexity of different processes despite the underlying phenomena being so different in nature? The field of computational mechanics offers an answer to these questions by identifying complexity with the amount of memory required for faithful prediction. While traditionally conceived with classical memory in mind this framework has now been extended to permit for quantum encoding of memory. Not only does this change our understanding of what is complex but also reveals potential for technological applications as some processes that are exceedingly costly to predict on a classical computer may be simulated efficiently on a quantum circuit.

This talk will offer an introduction to computational mechanics, the study of complexity in stochastic process. It will highlight how quantum memory can outperform classical physics when simulating stochastic processes, what aspects of quantum mechanics enable this advantage, and how our understanding of complexity changes when seen through the lens of quantum physics.

Speaker’s Bio

Dr. Felix Binder’s research lies in the areas of quantum information, thermodynamics, and complexity science. He finished his undergraduate studies at the University of Munich with a diploma in physics and an honours degree in digital technology management. After joining the University of Oxford as a Rhodes Scholar he defended his doctoral thesis on quantum thermodynamics in 2016. Since then he has been a research fellow in physics at Nanyang Technological University.