Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum-classical reinforcement algorithm for learning parity with noisy classical data and a noisy quantum machine

Kyungdeok Park, June‐Koo Kevin Rhee, Jonghun Park

Published Oct 23, 2019• 0 citations
OpenAlex

Related papers

Quantum-classical reinforcement parity learning from noisy classical data

Daniel K. Park, Jonghun Park, Suhwang Jeong +2 more

Bulletin of the American Physical Society•Mar 6, 2019•0 citations

Quantum-classical reinforcement learning for decoding noisy classical parity information

Daniel K. Park, Jonghun Park, June-Koo Kevin Rhee

arXiv:1910.00781•Quantum Machine Intelligence•Jun 1, 2020•3 citations

Variational Quantum Circuits for Deep Reinforcement Learning

Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi +3 more

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 necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

arXiv:1907.00397•IEEE Access•Jan 1, 2020•453 citations

Solving the Learning Parity with Noise Problem Using Quantum Algorithms

Bénédikt Tran, Serge Vaudenay B

Lecture notes in computer science•Jan 1, 2022•0 citations

A Survey on Quantum Reinforcement Learning

Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3 more

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.

arXiv:2211.03464•Nov 7, 2022•38 citations

Quantum-classical hybrid algorithms for noisy quantum computers

光祐 御手洗

•Mar 25, 2020•0 citations
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 necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.
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.