TorchQuantumDistributed
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909921428111360 |
|---|---|
| author | Knitter, Oliver Mei, Jonathan Yamada, Masako Roetteler, Martin |
| author_facet | Knitter, Oliver Mei, Jonathan Yamada, Masako Roetteler, Martin |
| contents | TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TorchQuantumDistributed Knitter, Oliver Mei, Jonathan Yamada, Masako Roetteler, Martin Quantum Physics Computational Engineering, Finance, and Science Machine Learning TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts. |
| title | TorchQuantumDistributed |
| topic | Quantum Physics Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2511.19291 |