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Quantum Algorithms

Conjugates, Filters and Quantum Mechanics

arXiv
Authors: Alexander Wilce

Year

2012

Paper ID

8621

Status

Preprint

Abstract Read

~2 min

Abstract Words

240

Citations

N/A

Abstract

The Jordan structure of finite-dimensional quantum theory is derived, in a conspicuously easy way, from a few simple postulates concerning abstract probabilistic models (each defined by a set of basic measurements and a convex set of states). The key assumption is that each system A can be paired with an isomorphic textit{conjugate} system, overline{A}, by means of a non-signaling bipartite state ηA perfectly and uniformly correlating each basic measurement on A with its counterpart on overline{A}. In the case of a quantum-mechanical system associated with a complex Hilbert space mathcal H, the conjugate system is that associated with the conjugate Hilbert space overline{mathcal H}, and ηA corresponds to the standard maximally entangled EPR state on {mathcal H} otimes overline{mathcal H}. A second ingredient is the notion of a textit{reversible filter}, that is, a probabilistically reversible process that independently attenuates the sensitivity of detectors associated with a measurement. In addition to offering more flexibility than most existing reconstructions of finite-dimensional quantum theory, the approach taken here has the advantage of not relying on any form of the "no restriction" hypothesis. That is, it is not assumed that arbitrary effects are physically measurable, nor that arbitrary families of physically measurable effects summing to the unit effect, represent physically accessible observables. An appendix shows how a version of Hardy's "subspace axiom" can replace several assumptions native to this paper, although at the cost of disallowing superselection rules.

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  • It adds a 2012 reference point for readers tracking recent quantum research.
  • The Jordan structure of finite-dimensional quantum theory is derived, in a conspicuously easy way, from a few simple postulates concerning abstract probabilistic models (each...

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