arXiv subjects

Aitor Morais

Publications and source records attributed to Aitor Morais.

2 recordsLinked to original sources

arXiv2026

autotn: Automata-inspired construction of tensor-network operators from symbolic local rules

Matrix product operators (MPOs) are a standard representation for structured operators in tensor-network simulations and quantum computing applications. However, converting symbolic descriptions of Hamiltonians and cost functions into MPO representations often requires substantial manual implementation, making the process error-prone and difficult to reproduce. This paper presents autotn, an open-source Python software tool that converts symbolic operator rules into MPO representations. Users define local operators and interaction rules in a high-level format, and the software generates the corresponding MPO along with validation utilities. In doing so, the software follows an automata-inspired approach that identifies repeated patterns in the symbolic description of the operator and reuses them to build a compact tensor-network representation, avoiding the need to construct each tensor by hand. The current implementation of autotn supports both diagonal operators, commonly encountered in combinatorial optimization problems, and general local matrix operators used in quantum many-body physics. The software is demonstrated on Max-Cut cost operators, long-range XX+YY+Z spin Hamiltonians, and quantum clock models. By providing a reproducible workflow from symbolic model specification to validated MPO construction, autotn reduces the engineering effort required to develop, verify, and compare MPO constructions.

cs.MS↗

arXiv2025

Comparative Analysis of Classical and Quantum-Inspired Solvers: A Preliminary Study on the Weighted Max-Cut Problem

Combinatorial optimization is essential across numerous disciplines. Traditional metaheuristics excel at exploring complex solution spaces efficiently, yet they often struggle with scalability. Deep learning has become a viable alternative for quickly generating high-quality solutions, particularly when metaheuristics underperform. In recent years, quantum-inspired approaches such as tensor networks have shown promise in addressing these challenges. Despite these advancements, a thorough comparison of the different paradigms is missing. This study evaluates eight algorithms on Weighted Max-Cut graphs ranging from 10 to 250 nodes. Specifically, we compare a Genetic Algorithm representing metaheuristics, a Graph Neural Network for deep learning, and the Density Matrix Renormalization Group as a tensor network approach. Our analysis focuses on solution quality and computational efficiency (i.e., time and memory usage). Numerical results show that the Genetic Algorithm achieves near-optimal results for small graphs, although its computation time grows significantly with problem size. The Graph Neural Network offers a balanced solution for medium-sized instances with low memory demands and rapid inference, yet it exhibits more significant variability on larger graphs. Meanwhile, the Tensor Network approach consistently yields high approximation ratios and efficient execution on larger graphs, albeit with increased memory consumption.

cs.ET↗