# Variational Quantum Soft Actor-Critic for Robotic Arm Control

arXiv: https://arxiv.org/abs/2212.11681 · HTML: https://mlsanity.com/paper/2212.11681/

## Key facts

- Cited by 8 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 20 Dec 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Actor-Critic Reinforcement Learning for Control with Stability Guarantee](https://mlsanity.com/paper/2004.14288/) (2020)
- [Quantum Deep Reinforcement Learning for Robot Navigation Tasks](https://mlsanity.com/paper/2202.12180/) (2022)
- [Variational Quantum Soft Actor-Critic](https://mlsanity.com/paper/2112.11921/) (2021)
- [Reinforcement Learning: Prediction, Control and Value Function Approximation](https://mlsanity.com/paper/1908.10771/) (2019)

## Built on by (most cited first, 8 of 8)

- [Asynchronous training of quantum reinforcement learning](https://mlsanity.com/paper/2301.05096/) (2023)
- [Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars](https://mlsanity.com/paper/2311.12875/) (2023)
- [A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning](https://mlsanity.com/paper/2405.12354/) (2024)
- [Quantum deep Q learning with distributed prioritized experience replay](https://mlsanity.com/paper/2304.09648/) (2023)
- [Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components](https://mlsanity.com/paper/2511.17112/) (2025)
- [Quantum deep reinforcement learning for humanoid robot navigation task](https://mlsanity.com/paper/2509.11388/) (2025)
- [An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning](https://mlsanity.com/paper/2311.06576/) (2023)
- [Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations](https://mlsanity.com/paper/2606.10448/) (2026)

## Abstract (arXiv)

Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control - namely Soft Actor-Critic. Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further promising developments.