Information ratchets exploiting spatially structured information reservoirs

TL;DR

This paper investigates how the spatial structure of information reservoirs influences their ability to extract work from heat baths, revealing critical dimensions that determine optimal exploration strategies and performance.

Contribution

It derives exact recurrence probabilities in d-dimensional reservoirs, showing how structure impacts work extraction and phase behavior, with implications for computation.

Findings

Performance depends on reservoir dimension: below 3, exploration is driven; above 4, diffusion is optimal.

Exact recurrence probabilities are derived for d-dimensional reservoirs.

Critical dimensions influence the efficiency of information ratchets.

Abstract

Information ratchets extract work from heat baths using low entropy information reservoirs. We find that the structure of the reservoirs affects both their performance and phase diagram. By deriving exact probabilities of recurrence in d-dimensional reservoirs we calculate the net extraction of work. Performance is characterised by two critical dimensions (i) For d < 3 driven exploration of the reservoir is essential to extract work (ii) For d > 4 purely diffusive exploration is optimal. This has important consequences for computation where sequential operations constitute a 1D path.