# TorchQuantumDistributed

arXiv: https://arxiv.org/abs/2511.19291 · HTML: https://mlsanity.com/paper/2511.19291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Signal Processing on Graphs: Causal Modeling of Unstructured Data](https://mlsanity.com/paper/1503.00173/) (2015)
- [cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science](https://mlsanity.com/paper/2308.01999/) (2023)
- [Hybrid quantum programming with PennyLane Lightning on HPC platforms](https://mlsanity.com/paper/2403.02512/) (2024)
- [Q#: Enabling scalable quantum computing and development with a high-level domain-specific language](https://mlsanity.com/paper/1803.00652/) (2018)
- [ProjectQ: An Open Source Software Framework for Quantum Computing](https://mlsanity.com/paper/1612.08091/) (2016)
- [Retentive Neural Quantum States: Efficient Ans"atze for Ab Initio Quantum Chemistry](https://mlsanity.com/paper/2411.03900/) (2024)

## Abstract (arXiv)

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.