8 citations · 10 across the 14 of their papers we have counts for
4 papers · 1 filter
Is stochastic thermodynamics the key to understanding the energy costs of computation?
David Wolpert, Jan Korbel, Christopher Lynn +16
The relationship between the thermodynamic and computational characteristics of dynamical physical systems has been a major theoretical interest since at least the 19th century, an…
Ultimate limit on learning non-Markovian behavior: Fisher information rate and excess information
Paul M. Riechers
We address the fundamental limits of learning unknown parameters of any stochastic process from time-series data, and discover exact closed-form expressions for how optimal inferen…
Thermodynamically ideal quantum-state inputs to any device
Paul M. Riechers, Chaitanya Gupta, Artemy Kolchinsky +1
We investigate and ascertain the ideal inputs to any finite-time thermodynamic process. We demonstrate that the expectation values of entropy flow, heat, and work can all be determ…
Complexity-calibrated Benchmarks for Machine Learning Reveal When Next-Generation Reservoir Computer Predictions Succeed and Mislead
Sarah E. Marzen, Paul M. Riechers, James P. Crutchfield
Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form…