# Variational Quantum Circuits for Deep Reinforcement Learning

@article{Chen2020VariationalQC, title={Variational Quantum Circuits for Deep Reinforcement Learning}, author={Samuel Yen-Chi Chen and Chao-Han Huck Yang and Jun Qi and Pin-Yu Chen and Xiaoli Ma and Hsi-Sheng Goan}, journal={IEEE Access}, year={2020}, volume={8}, pages={141007-141024} }

The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is…

## 105 Citations

### Reinforcement Learning with Quantum Variational Circuits

- Computer Science, PhysicsAAAI 2020
- 2020

Results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space.

### Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

- Computer Science
- 2021

A training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm and shows when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments.

### Quantum Architecture Search via Deep Reinforcement Learning

- Computer Science, PhysicsArXiv
- 2021

A quantum architecture search framework with the power of deep reinforcement learning (DRL) to address the challenge of generation of quantum gate sequences for multiqubit GHZ states without encoding any knowledge of quantum physics in the agent.

### Quantum Architecture Search via Continual Reinforcement Learning

- Computer ScienceArXiv
- 2021

The Probabilistic Policy Reuse with deep Q-learning (PPR-DQL) framework is presented and it is demonstrated that the RL agent with PPR was able to find the quantum gate sequence to generate the two-qubit Bell state faster than the agent that was trained from scratch.

### Parametrized Quantum Policies for Reinforcement Learning

- Computer ScienceNeurIPS
- 2021

This work proposes a hybrid quantum-classical reinforcement learning model using very few qubits, which it is shown can be effectively trained to solve several standard benchmarking environments and formally proves the ability of parametrized quantum circuits to solve certain learning tasks that are intractable to classical models.

### Quantum Computing Aided Machine Learning Through Quantum State Fidelity

- Computer Science
- 2021

This work proposes a quantum deep learning architecture and demonstrates the quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling and outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

### Variational quantum policies for reinforcement learning

- Computer ScienceArXiv
- 2021

This work investigates how to construct and train reinforcement learning policies based on variational quantum circuits, and proposes and shows the existence of task environments with a provable separation in performance between quantum learning agents and any polynomial-time classical learner.

### Quantum Enhancements for Deep Reinforcement Learning in Large Spaces

- Computer Science
- 2020

This work studies the state-of-the-art neural-network approaches for reinforcement learning with quantum enhancements in mind and demonstrates the substantial learning advantage that models with a sampling bottleneck can provide over conventional neural network architectures in complex learning environments.

### Variational quantum reinforcement learning via evolutionary optimization

- Computer Science, PhysicsMach. Learn. Sci. Technol.
- 2022

A hybrid framework is proposed where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

### Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

- Computer ScienceArXiv
- 2021

It is found that reinforcement learning augmented optimizers consistently outperform gradient descent in noisy environments.

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