SciPost Submission Page
The ITransverse.jl library for transverse tensor network contractions
by Stefano Carignano
Submission summary
| Authors (as registered SciPost users): | Stefano Carignano |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2509.03699v1 (pdf) |
| Code repository: | https://github.com/starsfordummies/ITransverse.jl |
| Code version: | 0.24.1 |
| Code license: | APACHE 2.0 |
| Date accepted: | Dec. 2, 2025 |
| Date submitted: | Sept. 18, 2025, 10:56 a.m. |
| Submitted by: | Stefano Carignano |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| Approach: | Computational |
Abstract
Transverse contraction methods are extremely promising tools for the efficient contraction of tensor networks associated with the time evolution of quantum many-body systems, allowing in some cases to circumvent the entanglement barrier that would normally prevent the study of quantum dynamics with classical resources. We present here the ITransverse.jl package, written in Julia and based on ITensors.jl, containing several of these high-level algorithms, including novel prescriptions for efficient truncations of temporal matrix product states.
Current status:
Editorial decision:
For Journal SciPost Physics Codebases: Publish
(status: Editorial decision fixed and (if required) accepted by authors)
Reports on this Submission
Report #1 by Atsushi Ueda (Referee 1) on 2025-11-13 (Invited Report)
Strengths
Weaknesses
Report
This paper instead represents a time-evolution of the states by evaluating a 2d space-time tensor network. The main advantage of this view is that contracting the tensors in the space direction often can circumvent the entanglement barrier. This allows us to access the quantum dynamics way beyond the traditionally achievable time range. The package provides a several intuitive way for simulating this state-of-art technique, opening up the new avenue for simulating quantum quenches and such.
The paper provides extensive explanation of both theoretical background and hands on benchmarking codes(also lots thereof in the github repo). I believe this paper matches all the criteria for this journal.
Requested changes
I am very happy with the current manuscript.
Recommendation
Publish (surpasses expectations and criteria for this Journal; among top 10%)
