Architecture of Quantum Neural Networks: Design and Implementation

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a promising paradigm shift in artificial intelligence, leveraging the unique properties of quantum mechanics to enhance computational capabilities. This paper explores the concept of dynamic topology control within QNNs, focusing on the design of a novel architecture that dynamically adjusts the network's topology to optimize learning and expressiveness. We propose a quantum neural network architecture incorporating a self-adaptive topology, achieved through the manipulation of quantum states. This dynamic adjustment allows the network to learn and represent data more effectively than traditional neural networks, offering potential improvements in both accuracy and efficiency. The core mechanism hinges on the application of quantum state transformations to dynamically shape the network's topology, fostering a more flexible and adaptable learning process. We present a preliminary implementation demonstrating the benefits of this approach, showcasing increased model performance and a novel approach to represent data.

Nasrullah Masud, Abdus Sobhan, Md Tanvir Islam Sourav +1 more

Seungcheol Oh, Emily Jimin Roh, Athanasios V. Vasilakos +2 more

Quantum neural networks (QNNs), typically implemented via parameterized quantum circuits (PQCs), offer a potential route to quantum advantage in learning. We review recent progress through the Fourier structure of PQCs: the encoding Hamiltonian fixes the accessible frequency set, while training estimates the corresponding Fourier coefficients. This perspective explains how QNNs can mitigate the spectral bias that limits classical deep neural networks and motivates task-specialized designs. In particular, quantum ordinary differential equation (QODE) solver, quantum convolutional neural networks (QCNNs) and quantum reinforcement learning (QRL) exploit frequency pinching to better capture high-frequency image features and structured value landscapes. We further discuss trainability on noisy intermediate-scale quantum (NISQ) hardware, emphasizing Lie-algebraic and Fourier views of barren plateaus and practical mitigation strategies. Together, the Fourier and Lie perspectives yield concrete design principles and motivate quantum-native architectures built around spectral bias avoidance rather than classical imitation. Quantum neural networks (QNNs), implemented via parameterized quantum circuits (PQCs), promise a path to quantum advantage in learning by addressing the limitations of classical deep neural networks. Here, the authors explore the Fourier structure of PQCs to mitigate spectral bias, enhancing applications like quantum ODE solvers and quantum reinforcement learning, with implications for designing quantum-native architectures.

Elizabeth Behrman, L.R. Nash, James E. Steck +2 more

Mohammad R. Khosravi, Maryam Zekri

Jincheng Zhang

This paper explores the design and implementation of quantum neural network (QNN) architectures leveraging the unique properties of topological qubits. Traditional QNNs face significant challenges regarding decoherence and scalability. Utilizing the inherent robustness of topological qubits – specifically their protection against local perturbations – presents a novel approach to constructing more reliable and efficient QNNs. We propose a framework for building QNNs based on topologically protected quantum bits, outlining the core mechanisms for information processing and demonstrating the potential for enhanced training stability. The architecture emphasizes the use of braiding operations to induce entanglement and the subsequent application of quantum algorithms for learning and inference. This work addresses a critical need for robust quantum computing platforms suitable for complex neural network training, moving beyond the limitations of current approaches. The presented architecture offers a pathway towards scalable and fault-tolerant QNNs, representing a significant step in realizing the full potential of quantum computation for artificial intelligence.