Details
| Original language | English |
|---|---|
| Article number | 1022 |
| Journal | Nature Communications |
| Volume | 9 |
| Issue number | 1 |
| Publication status | Published - 9 Mar 2018 |
| Externally published | Yes |
Abstract
Maximum-entropy ensembles are key primitives in statistical mechanics. Several approaches have been developed in order to justify the use of these ensembles in statistical descriptions. However, there is still no full consensus on the precise reasoning justifying the use of such ensembles. In this work, we provide an approach to derive maximum-entropy ensembles, taking a strictly operational perspective. We investigate the set of possible transitions that a system can undergo together with an environment, when one only has partial information about the system and its environment. The set of these transitions encodes thermodynamic laws and limitations on thermodynamic tasks as particular cases. Our main result is that the possible transitions are exactly those that are possible if both system and environment are assigned the maximum-entropy state compatible with the partial information. This justifies the overwhelming success of such ensembles and provides a derivation independent of typicality or information-theoretic measures.
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In: Nature Communications, Vol. 9, No. 1, 1022, 09.03.2018.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Statistical ensembles without typicality
AU - Boes, Paul
AU - Wilming, Henrik
AU - Eisert, Jens
AU - Gallego, Rodrigo
N1 - Funding Information: We thank H. Tasaki for comments. This work has been supported by the ERC (TAQ), the DFG (GA 2184/2-1, CRC 183, B02), the Studienstiftung des Deutschen Volkes, the EU (AQuS), and the COST action MP1209 on quantum thermodynamics.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - Maximum-entropy ensembles are key primitives in statistical mechanics. Several approaches have been developed in order to justify the use of these ensembles in statistical descriptions. However, there is still no full consensus on the precise reasoning justifying the use of such ensembles. In this work, we provide an approach to derive maximum-entropy ensembles, taking a strictly operational perspective. We investigate the set of possible transitions that a system can undergo together with an environment, when one only has partial information about the system and its environment. The set of these transitions encodes thermodynamic laws and limitations on thermodynamic tasks as particular cases. Our main result is that the possible transitions are exactly those that are possible if both system and environment are assigned the maximum-entropy state compatible with the partial information. This justifies the overwhelming success of such ensembles and provides a derivation independent of typicality or information-theoretic measures.
AB - Maximum-entropy ensembles are key primitives in statistical mechanics. Several approaches have been developed in order to justify the use of these ensembles in statistical descriptions. However, there is still no full consensus on the precise reasoning justifying the use of such ensembles. In this work, we provide an approach to derive maximum-entropy ensembles, taking a strictly operational perspective. We investigate the set of possible transitions that a system can undergo together with an environment, when one only has partial information about the system and its environment. The set of these transitions encodes thermodynamic laws and limitations on thermodynamic tasks as particular cases. Our main result is that the possible transitions are exactly those that are possible if both system and environment are assigned the maximum-entropy state compatible with the partial information. This justifies the overwhelming success of such ensembles and provides a derivation independent of typicality or information-theoretic measures.
UR - http://www.scopus.com/inward/record.url?scp=85043979927&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-03230-y
DO - 10.1038/s41467-018-03230-y
M3 - Article
C2 - 29523848
AN - SCOPUS:85043979927
VL - 9
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 1022
ER -