# QAISim: A Toolkit for Modeling and Simulation of AI in Quantum Cloud Computing Environments

arXiv: https://arxiv.org/abs/2512.17918 · HTML: https://mlsanity.com/paper/2512.17918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [A Survey on Quantum Reinforcement Learning](https://mlsanity.com/paper/2211.03464/) (2022)
- [Quantum Computing: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2010.15559/) (2020)
- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [The Complexity of NISQ](https://mlsanity.com/paper/2210.07234/) (2022)
- [Quantum Cloud Computing: A Review, Open Problems, and Future Directions](https://mlsanity.com/paper/2404.11420/) (2024)
- [qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation](https://mlsanity.com/paper/2308.02536/) (2023)
- [QuNetSim: A Software Framework for Quantum Networks](https://mlsanity.com/paper/2003.06397/) (2020)
- [DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling](https://mlsanity.com/paper/2105.07526/) (2021)
- [Quantum Cloud Computing: Trends and Challenges](https://mlsanity.com/paper/2404.19612/) (2024)

## Abstract (arXiv)

Quantum computing offers new ways to explore the theory of computation via the laws of quantum mechanics. Due to the rising demand for quantum computing resources, there is growing interest in developing cloud-based quantum resource sharing platforms that enable researchers to test and execute their algorithms on real quantum hardware. These cloud-based systems face a fundamental challenge in efficiently allocating quantum hardware resources to fulfill the growing computational demand of modern Internet of Things (IoT) applications. So far, attempts have been made in order to make efficient resource allocation, ranging from heuristic-based solutions to machine learning. In this work, we employ quantum reinforcement learning based on parameterized quantum circuits to address the resource allocation problem to support large IoT networks. We propose a python-based toolkit called QAISim for the simulation and modeling of Quantum Artificial Intelligence (QAI) models for designing resource management policies in quantum cloud environments. We have simulated policy gradient and Deep Q-Learning algorithms for reinforcement learning. QAISim exhibits a substantial reduction in model complexity compared to its classical counterparts with fewer trainable variables.