Published January 2014 | Version v1
Journal article

Toward computability of trace distance discord

  • 1. NEST, Istituto Nanoscienze-CNR and Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Via Archirafi 36, I-90123 Palermo (Italy)
  • 2. QOLS, Blackett Laboratory, Imperial College London, London SW7 2BW (United Kingdom)
  • 3. NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126 Pisa (Italy)

Description

It is known that a reliable geometric quantifier of discord-like correlations can be built by employing the so-called trace distance, which is used to measure how far the state under investigation is from the closest 'classical-quantum' state. To date, the explicit calculation of this indicator for two qubits has only been accomplished for states where the reduced density matrix of the measured party is maximally mixed, a class that includes Bell-diagonal states. Here, we first reduce the required optimization for a general two-qubit state to the minimization of an explicit two-variable function. Using this framework, we show that the minimum can be analytically worked out in a number of relevant cases, including quantum-classical and X states. This provides an explicit and compact expression for the trace distance discord of an arbitrary state belonging to either of these important classes of density matrices. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/1367-2630/16/1/013038

Additional details

Publishing Information

Journal Title
New Journal of Physics
Journal Volume
16
Journal Issue
1
Journal Page Range
[23 p.]
ISSN
1367-2630

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
46049683
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
BELL THEOREM; CORRELATIONS; DENSITY MATRIX; GEOMETRY; MINIMIZATION; QUANTUM MECHANICS; QUANTUM STATES; QUBITS
Descriptors DEC
INFORMATION; MATHEMATICS; MATRICES; MECHANICS; OPTIMIZATION; QUANTUM INFORMATION