Pith. sign in

REVIEW 1 cited by

Symmetry and composition in probabilistic theories

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 0910.1527 v2 pith:M3QNMV5I submitted 2009-10-08 quant-ph

classification quant-ph
keywords quantumtheoriesworkapproachesfeaturesgroupinformationmonoidal
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

The past decade has seen a remarkable resurgence of the old programme of finding more or less a priori axioms for the mathematical framework of quantum mechanics. The new impetus comes largely from quantum information theory; in contrast to work in the older tradition, which tended to concentrate on structural features of individual quantum systems, the newer work is marked by an emphasis on systems in interaction. Within this newer work, one can discerne two distinct approaches: one is "top-down", and attempts to capture in category-theoretic terms what is distinctive about quantum information processing. The other is "bottom up", attempting to construct non-classical models and theories by hand, as it were, and then characterizing those features that mark out quantum-like behavior. This paper blends these approaches. We present a constructive, bottom-up recipe for building probabilistic theories having strong symmetry properties, using as data any uniform enlargement of the symmetric group $S(E)$ of any set, to a larger group $G(E)$. Subject to some natural conditions, our construction leads to a monoidal category of fully symmetric test spaces, in which the monoidal product is "non-signaling".

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generalized Probability Theory: notes for a short course

    quant-ph 2025-01 unverdicted novelty 3.0 of 10

    An expert survey presenting GPTs as generalized probability theory through Foulis-Randall test spaces and their linearized ordered-vector-space form.

Pith tools