Abstract
We experimentally demonstrate that highly structured distributions of work emerge during even the simple task of erasing a single bit. These are signatures of a refined suite of time-reversal symmetries in distinct functional classes of microscopic trajectories. As a consequence, we introduce a broad family of conditional fluctuation theorems that the component work distributions must satisfy. Since they identify entropy production, the component work distributions encode the frequency of various mechanisms of both success and failure during computing, as well giving improved estimates of the total irreversibly dissipated heat. This new diagnostic tool provides strong evidence that thermodynamic computing at the nanoscale can be constructively harnessed. We experimentally verify this functional decomposition and the new class of fluctuation theorems by measuring transitions between flux states in a superconducting circuit.
Highlights
Physics dictates that all computing is subject to spontaneous error
We experimentally demonstrated that work fluctuations generated by information engines are highly structured
They strictly obeyed a suite of time-reversal symmetries—the trajectory-class fluctuation theorems introduced here
Summary
Physics dictates that all computing is subject to spontaneous error. These days, this truism repeatedly reveals itself: despite the once-predictable miniaturization of nanoscale electronics, computing performance increases have dramatically slowed in the last decade or so. The following introduces trajectory class fluctuation theorems to do this by identifying the thermodynamic signature of successful and failed information processing It experimentally demonstrates how this is practically implemented in a new microscale platform for thermodynamic computing. His demon has led to the realization that information itself is physical [2,3,4]—or, most constructively, that information is a thermodynamic resource (see [5] and references therein) This opened up the new paradigm of thermodynamic computing [6] in which fluctuations play a positive role in efficient information processing on the nanoscale. We show that functional and nonfunctional informational-state evolutions can be identified by appropriate conditioning, and that their thermodynamics obey a suite of trajectory-class fluctuation theorems As such, the latter give accurate bounds on work, entropy production, and dissipation for computing subprocesses. To make direct contact with previous efforts, we demonstrate the tools on Landauer erasure [2] of a bit of information in a superconducting flux qubit
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
