Exact Separation of Words via Trace Geometry

2026-03-31

AI Summary

This study investigates whether two-way one-measurement quantum finite automata can deterministically and exactly distinguish between two input words that share the same abelianization. By reducing the problem to a trace-zero decision problem in SU(2), the authors introduce a slice-driven framework that integrates sub-abelian polynomials derived from prefix-statistical decomposition with slope specialization techniques. This approach yields an explicitly parameterized family of low-dimensional matrices and establishes computable quadratic trace identities alongside Laurent matrix sum-of-squares identities. The method achieves complete exact separation under explicit certification conditions, covering four challenging classes of positive word-difference instances for the first time. Numerical experiments confirm the efficacy of the proposed criteria and demonstrate the inherent limitations of approaches relying solely on finite-image testing.

Abstract

A basic question in the theory of two-state measure-once quantum finite automata (MO-QFAs) is whether two distinct input words can be separated with certainty. In the setting considered here, this exact separation problem reduces to a trace-vanishing question in \(SU(2)\): given distinct positive words \(u\) and \(v\), find matrices \(A,B\in SU(2)\) such that the evaluated trace of \(u^{-1}v\) is zero. The central difficulty lies in the genuinely nonabelian regime where \(u\) and \(v\) have the same abelianization, so the obvious commutative information disappears and the fine structure of the word must be connected to the geometry of representations. This paper develops a slice-driven framework for that task and proves exact separation for every hard positive-word difference covered by four explicit certified conditions, thereby reducing the problem to a sharply delimited residual super-degenerate class. The method extracts algebraic data from the positive-word difference and uses them to select explicit low-dimensional families in \(SU(2)^2\) on which the trace becomes computable. On the algebraic side, the metabelian polynomial is decomposed into explicit interval blocks determined by prefix statistics, and a suitable slope specialization preserves nontrivial information. On the analytic side, the paper derives a computable quadratic trace identity on a visible one-parameter family and complements it with a Laurent-matrix sum-of-squares identity in a parallel algebraic model. These certified criteria are already strong in numerical experiments. This paper also shows that no method based only on finitely many finite-image tests can be universal.

Problem

• quantum finite automata
• exact separation
• trace vanishing
• SU(2)
• nonabelian words

Innovation

• quantum finite automata
• trace geometry
• SU(2) representations
• metabelian polynomial
• exact word separation