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Properties and Applications of the Kirkwood-Dirac Distribution

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arxiv 2403.18899 v2 pith:O4U3IZS6 submitted 2024-03-27 quant-ph cond-mat.stat-mech

Properties and Applications of the Kirkwood-Dirac Distribution

classification quant-ph cond-mat.stat-mech
keywords distributionquantumstateskirkwood-diracmechanicsnon-positivityobservablesprobability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recent years have seen the Kirkwood-Dirac (KD) distribution come to the forefront as a powerful quasi-probability distribution for analysing quantum mechanics. The KD distribution allows tools from statistics and probability theory to be applied to problems in quantum-information processing. A notable difference to the Wigner function is that the KD distribution can represent a quantum state in terms of arbitrary observables. This paper reviews the KD distribution, in three parts. First, we present definitions and basic properties of the KD distribution and its generalisations. Second, we summarise the KD distribution's extensive usage in the study or development of measurement disturbance; quantum metrology; weak values; direct measurements of quantum states; quantum thermodynamics; quantum scrambling and out-of-time-ordered correlators; and the foundations of quantum mechanics, including Leggett-Garg inequalities, the consistent-histories interpretation and contextuality. We emphasise connections between operational quantum advantages and negative or non-real KD quasi-probabilities. Third, we delve into the KD distribution's mathematical structure. We summarise the current knowledge regarding the geometry of KD-positive states (the states for which the KD distribution is a classical probability distribution), describe how to witness and quantify KD non-positivity, and outline relationships between KD non-positivity, coherence and observables' incompatibility.

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Cited by 4 Pith papers

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    Temporal state tomography reconstructs multi-time quantum processes from temporal quasiprobability distributions via a Bloch-type representation and derives the associated sample complexity.