cff-version: 1.2.0
message: "If you use the software, data, figures, protocols, or derived results, cite release v1.1.0 and identify any later commit used; see CITATION.md for copy-ready formats."
title: "Representation Alignment in Commuting Quantum Boltzmann Machines"
type: software
authors:
  - family-names: Lin
    given-names: Ruge
    affiliation: "The Hong Kong University of Science and Technology (Guangzhou)"
version: 1.1.0
date-released: 2026-08-26
repository-code: "https://github.com/GoGoKo699/QBM-Representation-Alignment"
url: "https://github.com/GoGoKo699/QBM-Representation-Alignment"
license: BSD-3-Clause
abstract: >-
  Reproducible theory, experiments, and exact preparation-resource analyses for
  representation alignment in commuting quantum Boltzmann machines. The archive
  includes a prospectively frozen weighted sparse-Ising confirmation on a
  separately generated target ensemble and a separate exhaustive supporting
  study that records a graph-selection boundary.
keywords:
  - quantum Boltzmann machine
  - commuting Gibbs model
  - Gibbs-state preparation
  - sparse Ising optimization
  - representation alignment
  - natural gradient
  - graphical models
  - maximum spanning tree
  - treewidth
  - q-sample
  - reproducible research