# Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting

arXiv: https://arxiv.org/abs/2404.09213 · HTML: https://mlsanity.com/paper/2404.09213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Quantum Denoising Diffusion Models](https://mlsanity.com/paper/2401.07049/) (2024)
- [Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping](https://mlsanity.com/paper/2212.14807/) (2022)
- [Weight Re-Mapping for Variational Quantum Algorithms](https://mlsanity.com/paper/2306.05776/) (2023)

## Built on by (most cited first, 4 of 4)

- [VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?](https://mlsanity.com/paper/2607.11985/) (2026)
- [Quantum King-Ring Domination in Chess: A QAOA Approach](https://mlsanity.com/paper/2601.00318/) (2026)
- [Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study](https://mlsanity.com/paper/2602.16523/) (2026)
- [Illustration of Barren Plateaus in Quantum Computing](https://mlsanity.com/paper/2602.16558/) (2026)

## Abstract (arXiv)

To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub https://github.com/gstenzel/qandle and PyPI https://pypi.org/project/qandle/ .