tneq_qc: Tensor Network Engine toward Quantum: Yet Another Software But More Flexible, Learnable, and Distributable

Keywords: tensor networks, quantum tensor networks, differentiable learning, distributed contraction, quantum machine learning

TL;DR: tneq_qc provides a flexible differentiable and distributed software framework for quantum tensor networks.

Abstract: We present tneq_qc, a lightweight software framework for building, composing, contracting, and training quantum tensor networks. The framework combines a human-readable ASCII graph representation with scaled tensor abstractions, strategy-driven contraction, interchangeable PyTorch/JAX backends, and optional distributed execution. This design supports both rapid prototyping and end-to-end differentiable learning workflows for quantum-inspired models. We demonstrate the framework on three representative settings: tensor factorization, quantum tensor network equation matching, and quadratic-form density estimation. We further report distributed benchmark results showing that the framework can provide substantial acceleration in favorable regimes while also reducing memory usage for larger workloads.

Submission Number: 42