Operational Quantification of Continuous-Variable Quantum Resources

Area of Science:

• Quantum Information Science
• Quantum Optics
• Quantum Information Theory

Background:

• Quantum states are crucial for practical applications, necessitating methods to measure and compare diverse quantum resources.
• Existing resource quantification methods are often limited to finite-dimensional systems or lack operational connections.
• Continuous-variable (CV) quantum systems present unique challenges for resource quantification.

Purpose of the Study:

• To develop a universally applicable method for quantifying resources in continuous-variable quantum systems.
• To introduce a robustness measure that overcomes limitations of previous quantification approaches.
• To establish an operational interpretation and practical computability for quantum resource measures.

Main Methods:

• Introduction of a general resource quantification method based on the robustness measure for CV quantum systems.
• Demonstration of the measure's applicability to various resources like nonclassicality, entanglement, non-Gaussianity, and coherence.
• Establishing the robustness measure as a bona fide quantifier in convex resource theories.

Main Results:

• The robustness measure quantifies resources in CV systems, including optical nonclassicality, entanglement, genuine non-Gaussianity, and coherence.
• The measure has a direct operational interpretation as an advantage in channel discrimination tasks.
• The robustness is shown to be a well-behaved quantifier, unlike the standard robustness, and is directly observable via a single witness operator.

Conclusions:

• The developed robustness measure provides a powerful and versatile tool for quantifying quantum resources in CV systems.
• This method offers practical advantages, including direct observability and exact computability for several state classes.
• The findings advance the understanding and practical utilization of quantum resources in continuous-variable quantum information processing.