Reinforcement Learning with Deep Quantum Neural Networks

Hu, Wei; Hu, James Lee

doi:10.4236/jqis.2019.91001

Cited by 10 publications

(15 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article by Hu and Hu [HH19b] proposes distributional reinforcement learning (Dis-tRL) [Dab+17] for a photonic quantum computer. It is an extension of previous work of the authors [HH19a;HH19c].…”

Section: Algorithmicmentioning

confidence: 60%

See 1 more Smart Citation

A Survey on Quantum Reinforcement Learning

Meyer¹,

Ufrecht²,

Periyasamy³

et al. 2022

Preprint

View full text Add to dashboard Cite

Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning (our interpretation of this term will be clarified below), we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.

show abstract

Section: Algorithmicmentioning

confidence: 60%

“…Reinforcement Learning with Deep Quantum Neural Networks, Summary. This paper by Hu and Hu [HH19c] is an extension of the authors' work [HH19d]. Similar to the previous work, a CV-QNN approximates the action-value function in Q-learning and actor-critic based RL.…”

Section: Algorithmicmentioning

confidence: 70%

A Survey on Quantum Reinforcement Learning

Meyer¹,

Ufrecht²,

Periyasamy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In our previous work, we used these quantum neural networks to study the contextual bandit problem, and to implement Q learning and actor-critic algo-Intelligent Control and Automation rithms [20] [21] [22]. In this paper, we direct our attention on implementation of the QR distributional Q learning algorithm with these networks.…”

Section: Related Workmentioning

confidence: 99%

“…A grid world environment is used to evaluate the performance of quantum QR distributional Q learning, which is also used in our previous work [21] [22]. It has a size of 2 × 3 and can be configured into two modes: slippery or not slippery.…”

Section: Grid Worldmentioning

confidence: 99%

Distributional Reinforcement Learning with Quantum Neural Networks

Hu¹,

Hu²

2019

ICA

Self Cite

View full text Add to dashboard Cite

Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy. However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks. The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL. This paper builds on our recent work investigating the quantum approach towards RL. Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network. This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm. It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning. Efficient exploration and balancing of exploitation and exploration are major challenges in RL. Previous work has shown that more informative actions can be taken with a distributional perspective. Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.

show abstract

“…Already work has been done to develop quantum GANs (Zoufal, Lucchi, and Woerner 2019) and quantum CNNs (Cong, Choi, and Lukin 2019). Recently, the quantum RL field has been expanding with a variety of approaches such as using Grover Iterations (Ganger and Hu 2019) and CV photonic gates (Hu and Hu 2019) to solve gridworld environments. Other work has been done to envisage quantum computing as a RL problem (Khairy et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning with Quantum Variational Circuit

Lockwood

2020

AIIDE

View full text Add to dashboard Cite

The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.

show abstract

Reinforcement Learning with Deep Quantum Neural Networks

Cited by 10 publications

References 27 publications

A Survey on Quantum Reinforcement Learning

A Survey on Quantum Reinforcement Learning

Distributional Reinforcement Learning with Quantum Neural Networks

Reinforcement Learning with Quantum Variational Circuit

Contact Info

Product

Resources

About