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Quantum-enhanced computation enables decision-making and new paradigms for learning consequently transforming artificial intelligence (AI). This chapter explores the individual capabilities of Quantum Neural Networks (QNNs) and Quantum Reinforcement Learning (QRL) before delving into their synergies. QNNs enables advancements in deep learning applications leveraging quantum circuits to improve pattern recognition, optimization, and generative modeling. On other hand, QRL integrates quantum mechanics into reinforcement learning. This integration improves exploration, accelerate policy optimization, and improve decision-making in complex environments. First we establish foundational principles of QNNs and QRL and then provide a structured analysis of how these two fields can be combined. We examine theoretical synergies, such as using QNNs as function approximators in QRL and leveraging quantum-enhanced exploration strategies. Additionally, practical implementations, real-world applications, and current research frameworks are discussed. Finally, we highlight key challenges, including hardware limitations, scalability issues, and algorithmic constraints, while outlining future research directions. Furthermore, we explore practical implementations, including existing frameworks such as TensorFlow Quantum, Qiskit, and PennyLane, and discuss real-world applications in robotics, finance, autonomous systems, and drug discovery. A comparative analysis of classical and quantum reinforcement learning is presented, evaluating computation time, accuracy, and scalability. Additionally, we address key challenges such as quantum noise, decoherence, hardware limitations, and scalability issues, which hinder the widespread adoption of quantum AI. Finally, we outline future research directions, emphasizing advancements in error correction, hybrid quantum–classical models, and the long-term vision of generalized quantum AI. This chapter provides a comprehensive overview of QNNs and QRL, highlighting their transformative potential in revolutionizing artificial intelligence.

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

  • 章节

  • 发表日期：

    2025年

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  • Aanal Raval,
  • Ruchi Oza

摘要

Quantum-enhanced computation enables decision-making and new paradigms for learning consequently transforming artificial intelligence (AI). This chapter explores the individual capabilities of Quantum Neural Networks (QNNs) and Quantum Reinforcement Learning (QRL) before delving into their synergies. QNNs enables advancements in deep learning applications leveraging quantum circuits to improve pattern recognition, optimization, and generative modeling. On other hand, QRL integrates quantum mechanics into reinforcement learning. This integration improves exploration, accelerate policy optimization, and improve decision-making in complex environments. First we establish foundational principles of QNNs and QRL and then provide a structured analysis of how these two fields can be combined. We examine theoretical synergies, such as using QNNs as function approximators in QRL and leveraging quantum-enhanced exploration strategies. Additionally, practical implementations, real-world applications, and current research frameworks are discussed. Finally, we highlight key challenges, including hardware limitations, scalability issues, and algorithmic constraints, while outlining future research directions. Furthermore, we explore practical implementations, including existing frameworks such as TensorFlow Quantum, Qiskit, and PennyLane, and discuss real-world applications in robotics, finance, autonomous systems, and drug discovery. A comparative analysis of classical and quantum reinforcement learning is presented, evaluating computation time, accuracy, and scalability. Additionally, we address key challenges such as quantum noise, decoherence, hardware limitations, and scalability issues, which hinder the widespread adoption of quantum AI. Finally, we outline future research directions, emphasizing advancements in error correction, hybrid quantum–classical models, and the long-term vision of generalized quantum AI. This chapter provides a comprehensive overview of QNNs and QRL, highlighting their transformative potential in revolutionizing artificial intelligence.