Code and data from: Quantum algorithms for equational reasoning

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.