The reviewed record of science sign in
Pith

arxiv: 2001.00862 · v1 · pith:HAUJOY5O · submitted 2020-01-03 · quant-ph · cs.CL

Meaning updating of density matrices

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:HAUJOY5Orecord.jsonopen to challenge →

classification quant-ph cs.CL
keywords meaningquantumupdatedensitydiscocircmeaningsdiscocatfoundations
0
0 comments X
read the original abstract

The DisCoCat model of natural language meaning assigns meaning to a sentence given: (i) the meanings of its words, and, (ii) its grammatical structure. The recently introduced DisCoCirc model extends this to text consisting of multiple sentences. While in DisCoCat all meanings are fixed, in DisCoCirc each sentence updates meanings of words. In this paper we explore different update mechanisms for DisCoCirc, in the case where meaning is encoded in density matrices---which come with several advantages as compared to vectors. Our starting point are two non-commutative update mechanisms, borrowing one from quantum foundations research, from Leifer and Spekkens. Unfortunately, neither of these satisfies any desirable algebraic properties, nor are internal to the meaning category. By passing to double density matrices we do get an elegant internal diagrammatic update mechanism. We also show that (commutative) spiders can be cast as an instance of the Leifer-Spekkens update mechanism. This result is of interest to quantum foundations, as it bridges the work in Categorical Quantum Mechanics (CQM) with that on conditional quantum states. Our work also underpins implementation of text-level natural language processing on quantum hardware (a.k.a. QNLP), for which exponential space-gain and quadratic speed-up have previously been identified.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

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

  1. GPU-Accelerated Quantum Simulation: Empirical Backend Selection, Gate Fusion, and Adaptive Precision

    quant-ph 2026-04 unverdicted novelty 5.0

    A new GPU quantum simulator framework achieves 64x-146x speedups for 20-28 qubit circuits via backend selection, gate fusion, and adaptive precision while integrating with Qiskit and others.