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

Information systems engineering and management

Advancing AI with Quantum Computing: Synergies Between Quantum Neural Networks and Quantum Reinforcement Learning

Aanal Sanjivbhai Raval, Ruchi Oza

Published Jan 1, 2025• 2 citations
Publisher
DOI
10.1007/978-3-031-99786-0_14

Related papers

Quantum Reinforcement Learning: Advancing AI Agents Through Quantum Computing

Ved Seetohul, Hamid Jahankhani, Stefan Kendzierskyj +1 more

Space Law and Policy•Jan 1, 2024•10 citations

Quantum Reinforcement Learning: An Overview

Gyu Seon Kim, Soohyun Park, Joongheon Kim

Recent research in quantum computing has gained significant momentum, promising transformative advances across various fields, including artificial intelligence (AI). This paper focuses on quantum reinforcement learning (QRL), which utilizes quantum neural network (QNN) to address the inherent chal-lenges in conventional reinforcement learning (RL) frameworks. For a comprehensive examination of QRL, in this paper, the structure of QNN is explored in-depth, including the functional roles of key quantum circuit gates such as the unitary, rotation, controlled-X, and Hadamard gates. This paper also discusses the primary components of QNN, namely state encoding, parameterized quantum circuits (PQC), and measurement processes. Furthermore, the application of IBM qiskit for the visualization of quantum states and circuits is highlighted, providing practical insights into the deployment of quantum principles in RL. Consequently, this paper encapsulates the potential of QRL to revolutionize AI, positioning it as a pivotal area of future research and development in quantum computing.

International Conference on Information and Communication Technology Convergence (ICTC)•Oct 16, 2024•4 citations

Quantum Task Impact Learning (QTIL)Algorithm on Quantum Reinforcement Learning

R. Palanivel, P. Muthulakshmi

Abstract Quantum reinforcement learning (QRL) can change computational paradigms through quantum parallelism. A novel algorithm called Quantum Task Impact Learning (QTIL) is coined in this paper, specially designed for the Cart-Pole environment. QTIL combines neural networks with quantum circuits to improve decision-making in dynamic environments. By manipulating quantum states iteratively (ψ) via a policy circuit (P) and computing temporal differences (δ) to update priorities depending on task importance, efficient adaptation to Cart-Pole dynamics is ensured by QTIL. Its prioritized experience replays mechanism guides quantum circuit parameter updates across minibatches thus enabling focused learning. As such, convergence can be achieved faster, and performance enhanced due to the role played by quantum states in Cart-Pole reinforcement learning. The fusion of quantum and artificial intelligence methods into QTIL opens possibilities for advances in quantum-enhanced machine learning especially in complicated issues like Cart-Pole. Potential exploration and optimization of QTIL may greatly enhance synergy between these two quantum computing paradigms leading to breakthroughs in reinforcement learning that are nothing short of transformative.

•Aug 13, 2024•1 citation

Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing

Leonhard Kunczik

Springer eBooks•Jan 1, 2022•3 citations

Advent of Quantum Computing and AI: Synergies and Implications

Rishi darji, Kush Agarwal

•Jan 1, 2023•0 citations

Quantum Jump to Connected Worlds: Paradigm Shifts in Mobility and Network Applications via Quantum Neural Networks

Hyunsoo Lee, Gyu Seon Kim, Emily Jimin Roh +4 more

With the rapid advancement of quantum and artificial intelligence technologies, there is a growing trend to merge these two fields, a movement that is gaining momentum across both industry and academia. This emerging quantum artificial intelligence (QAI) is expected to achieve optimization and performance improvements in various mobility and network applications. The purpose of this article is to introduce quantum neural networks (QNNs) and explore applications of QAI in distributed network use cases. We begin by thoroughly investigating how QAI is being researched in the industry. Next, we discuss the fundamentals of quantum computing and QNNs. Following that, we examine QNN-based neural learning models for network and mobility applications, including quantum reinforcement learning, quantum convolutional neural networks, and quantum distributed learning. Finally, we consider the implications that QAI may have for future network and mobility applications.

IEEE Network•Jan 1, 2026•0 citations
Recent research in quantum computing has gained significant momentum, promising transformative advances across various fields, including artificial intelligence (AI). This paper focuses on quantum reinforcement learning (QRL), which utilizes quantum neural network (QNN) to address the inherent chal-lenges in conventional reinforcement learning (RL) frameworks. For a comprehensive examination of QRL, in this paper, the structure of QNN is explored in-depth, including the functional roles of key quantum circuit gates such as the unitary, rotation, controlled-X, and Hadamard gates. This paper also discusses the primary components of QNN, namely state encoding, parameterized quantum circuits (PQC), and measurement processes. Furthermore, the application of IBM qiskit for the visualization of quantum states and circuits is highlighted, providing practical insights into the deployment of quantum principles in RL. Consequently, this paper encapsulates the potential of QRL to revolutionize AI, positioning it as a pivotal area of future research and development in quantum computing.
Abstract Quantum reinforcement learning (QRL) can change computational paradigms through quantum parallelism. A novel algorithm called Quantum Task Impact Learning (QTIL) is coined in this paper, specially designed for the Cart-Pole environment. QTIL combines neural networks with quantum circuits to improve decision-making in dynamic environments. By manipulating quantum states iteratively (ψ) via a policy circuit (P) and computing temporal differences (δ) to update priorities depending on task importance, efficient adaptation to Cart-Pole dynamics is ensured by QTIL. Its prioritized experience replays mechanism guides quantum circuit parameter updates across minibatches thus enabling focused learning. As such, convergence can be achieved faster, and performance enhanced due to the role played by quantum states in Cart-Pole reinforcement learning. The fusion of quantum and artificial intelligence methods into QTIL opens possibilities for advances in quantum-enhanced machine learning especially in complicated issues like Cart-Pole. Potential exploration and optimization of QTIL may greatly enhance synergy between these two quantum computing paradigms leading to breakthroughs in reinforcement learning that are nothing short of transformative.
With the rapid advancement of quantum and artificial intelligence technologies, there is a growing trend to merge these two fields, a movement that is gaining momentum across both industry and academia. This emerging quantum artificial intelligence (QAI) is expected to achieve optimization and performance improvements in various mobility and network applications. The purpose of this article is to introduce quantum neural networks (QNNs) and explore applications of QAI in distributed network use cases. We begin by thoroughly investigating how QAI is being researched in the industry. Next, we discuss the fundamentals of quantum computing and QNNs. Following that, we examine QNN-based neural learning models for network and mobility applications, including quantum reinforcement learning, quantum convolutional neural networks, and quantum distributed learning. Finally, we consider the implications that QAI may have for future network and mobility applications.