The thermodynamic meaning of negative entropy

By Lídia del Rio, Johan Aberg, Renato Renner, Oscar C. O. Dahlsten, Vlatko Vedral. Nature, 2010

384 citations recorded by the source corpus.

The heat generated by computations is not only an obstacle to circuit miniaturization but also a fundamental aspect of the relationship between information theory and thermodynamics. In principle, reversible operations may be performed at no energy cost; given that irreversible computations can always be decomposed into reversible operations followed by the erasure of data, the problem of calculating their energy cost is reduced to the study of erasure. Landauer’s principle states that the erasure of data stored in a system has an inherent work cost and therefore dissipates heat. However, this consideration assumes that the information about the system to be erased is classical, and does not extend to the general case where an observer may have quantum information about the system to be erased, for instance by means of a quantum memory entangled with the system. Here we show that the standard formulation and implications of Landauer’s principle are no longer valid in the presence of quantum information. Our main result is that the work cost of erasure is determined by the entropy of the system, conditioned on the quantum information an observer has about it. In other words, the more an observer knows about the system, the less it costs to erase it. This result gives a direct thermodynamic significance to conditional entropies, originally introduced in information theory. Furthermore, it provides new bounds on the heat generation of computations: because conditional entropies can become negative in the quantum case, an observer who is strongly correlated with a system may gain work while erasing it, thereby cooling the environment.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b082233b2d7c38a7e1aa18beddfdd6af9e087abd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Fernando G. S. L. Brandão
  2. Michal Horodecki
  3. Raam Uzdin
  4. Ronnie Kosloff
  5. Gernot Schaller
  6. Tobias Brandes
  7. Philipp Strasberg
  8. Jonathan Oppenheim

In Atomic and Molecular Physics, and Optics

  1. Fernando G. S. L. Brandão
  2. Ronnie Kosloff
  3. Gernot Schaller
  4. Tobias Brandes
  5. Philipp Strasberg
  6. Michal Horodecki
  7. Raam Uzdin
  8. Sai Vinjanampathy

In Statistical and Nonlinear Physics

  1. Tobias Brandes
  2. Philipp Strasberg
  3. Ronnie Kosloff
  4. Gernot Schaller
  5. Sai Vinjanampathy
  6. Janet Anders
  7. Raam Uzdin
  8. Massimiliano Esposito

Citations in the corpus