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Code and data from: Quantum algorithms for equational reasoning

Rattacaso, Davide12; Jaschke, Daniel123; Ballarin, Marco12; Siloi, Ilaria12; Montangero, Simone12

Published Apr 29, 2026 on Dryad. https://doi.org/10.5061/dryad.6m905qgd4

Data files

Apr 29, 2026 version files 9.49 GB

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Abstract

This dataset contains the source code, simulation data, and analysis scripts associated with the study "Quantum Algorithms for Equational Reasoning." The original research introduces quantum normal form reduction, a computational framework designed to address core problems in equational reasoning, such as the word problem (determining semantic equivalence between symbolic expressions), counting equivalent expressions, and analyzing the structural properties of equivalence classes. To facilitate reproducibility, this repository provides a Python-based implementation of a quantum-inspired version of the algorithm. The package transforms a string rewriting system into a 1D local Hamiltonian and simulates the proposed quantum algorithm using the "Quantum Tea Leaves" tensor network emulator. The dataset includes: 1) Source Code: The complete Python package and environment instructions required to reproduce the paper’s results. 2) Simulation Outputs: Raw tensor network files compatible with "Quantum Tea Leaves" and extracted observable expectation values. 3) Visualization: Scripts used to generate the paper's figures, along with the original figure files themselves.