Quantum Neural Networks (QNNs) are one of the most promising applications that can be implemented on NISQ-era quantum computers. In this study, we observe that QNNs often suffer from gate redundancy, which hugely declines the performance and accuracy of the network. Even state-of-the-art architecture search techniques like QuantumNAS do not completely alleviate this problem. Especially, we find that CNOT gates are major contributors to the execution delay and noise in quantum circuits, and there are many redundant CNOT gates in the QNN post-training. This motivates us to propose a novel distribution-based greedy-search circuit optimization technique that can be employed after the completion of the training process. Our technique significantly reduces the number of CNOT gates in QNNs without affecting the accuracy of the network. With this technique, we have achieved an average of 3× improvement in execution time while reaching a maximum of 12.4× improvement.
Quantum Neural Networks (QNNs) are one of the most promising applications that can be implemented on NISQ-era quantum computers. In this study, we observe that QNNs often suffer from gate redundancy, which hugely declines the performance and accuracy of the network. Even state-of-the-art architecture search techniques like QuantumNAS do not completely alleviate this problem. Especially, we find that CNOT gates are major contributors to the execution delay and noise in quantum circuits, and there are many redundant CNOT gates in the QNN post-training. This motivates us to propose a novel distribution-based greedy-search circuit optimization technique that can be employed after the completion of the training process. Our technique significantly reduces the number of CNOT gates in QNNs without affecting the accuracy of the network. With this technique, we have achieved an average of 3× improvement in execution time while reaching a maximum of 12.4× improvement.
The use of Quantum Neural Networks (QNN) that are analogous to classical neural networks, has greatly increased in the past decade owing to the growing interest in the field of Quantum Machine Learning (QML). A QNN consists of three major components: (i) data loading/encoding circuit, (ii) Parametric Quantum Circuit (PQC), and (iii) measurement operations. Under ideal circumstances the PQC of the QNN trains well, however that may not be the case for training under quantum hardware due to presence of different kinds of noise. Deeper QNNs with high depths tend to degrade more in terms of performance compared to shallower networks. This work aims to reduce depth and gate count of PQCs by replacing parametric gates with their approximate fixed non-parametric representations. We propose a greedy algorithm to achieve this such that the algorithm minimizes a distance metric based on unitary transformation matrix of original parametric gate and new set of non-parametric gates. From this greedy optimization followed by a few epochs of re-training, we observe roughly 14% reduction in depth and 48% reduction in gate count at the cost of 3.33# reduction in inferencing accuracy. Similar results are observed for a different dataset as well with different PQC structure.
Quantum neural architecture search aims to automate the design of parameterized quantum circuits (PQCs) within quantum neural networks (QNNs), where learning performance is highly sensitive to the placement and type of quantum gates. However, applying traditional NAS techniques to quantum circuits presents significant challenges, including barren plateaus, noise accumulation, device-specific constraints, and the high computational cost of independently evaluating all candidate architectures. To address these limitations, this paper proposes Q-RLONAS, a two-stage quantum NAS framework that integrates deep reinforcement learning (DRL) with one-shot NAS to efficiently explore and optimize PQC structures. In the Stage #1, DRL determines the optimal gate placement by leveraging a reward function that considers performance, noise, barren plateaus, and hardware constraints. In the Stage #2, a one-shot NAS method is employed to select effective quantum gate types using weight sharing within a supernet, significantly reducing training overhead. Experimental results on the mini-MNIST dataset demonstrate that Q-RLONAS achieves high accuracy while satisfying gate placement constraints, outperforming existing approaches in terms of efficiency for quantum circuit design.
The rapid advancement in Quantum Computing, particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum algorithms remain impractical due to the limitations of current NISQ devices. Hybrid quantum-classical neural networks (HQNNs) have emerged as a viable alternative, leveraging both quantum and classical computations to enhance machine learning capabilities. However, the constrained resources of NISQ devices, particularly the limited number of qubits, pose significant challenges for executing large-scale quantum circuits. This work addresses these current challenges by proposing a novel and practical methodology for quantum circuit cutting of HQNNs, allowing large quantum circuits to be executed on limited-qubit NISQ devices. Our approach not only preserves the accuracy of the original circuits but also supports the training of quantum parameters across all subcircuits, which is crucial for the learning process in HQNNs. We propose a cutting methodology for HQNNs that employs a greedy algorithm for identifying efficient cutting points, and the implementation of trainable subcircuits, all designed to maximize the utility of NISQ devices in HQNNs. The findings suggest that quantum circuit cutting is a promising technique for advancing QML on current quantum hardware, since the cut circuit achieves comparable accuracy and much lower qubit requirements than the original circuit. The code is available at https://github.com/eBrain4Everyone/QNN-Cutting.
This study explores various architectures of Quantum Neural Networks (QNN) that utilize quantum computing to enhance machine learning model performance. The architectures examined include Hybrid Quantum Neural Network (H-QNN), Deep Quantum Neural Network (DQNN), Quantum Convolutional Neural Network (QCNN), and Coherent Feed Forward Quantum Neural Network. Additionally, the EfficientSU2 architecture demonstrated superior performance in accuracy and cost reduction. Optimization techniques such as Adaptive Moment Estimation (ADAM), Analytic Quantum Gradient Descent (AQGD), and Nakanishi-Fujii-Todo (NFT) were integrated to improve training efficiency. The study results show that EfficientSU2 with 10,000 iterations yielded the best results in terms of cost and accuracy across all tested datasets, with accuracy improvements reaching up to 97%. In conclusion, the EfficientSU2 architecture and the applied optimization techniques significantly enhanced the processing of complex data, paving the way for substantial advancements in quantum-based artificial intelligence applications.
Quantum convolutional neural networks (QCNNs) are emerging as promising models in quantum machine learning, particularly for image classification and computer vision tasks. Recent developments include hybrid classical–quantum architectures, advanced quantum encoding methods, and novel circuit designs that improve data processing on Noisy Intermediate-Scale Quantum (NISQ) devices. However, practical implementation remains challenging due to circuit complexity, gate count, qubit connectivity, and hardware noise, which limit scalability and performance. Consequently, quantum circuit optimization has become essential for reducing resource requirements and improving classification accuracy. This study presents a systematic literature review of 40 research papers published between 2014 and 2025. The review covers QCNNs together with closely related quantum neural network (QNN) models and quantum circuit optimization studies, since circuit-optimization techniques are frequently developed for QNNs more broadly rather than for QCNN architectures in isolation. Within this scope, it examines network architectures, encoding strategies, application domains, and optimization techniques, with particular attention to heuristic and metaheuristic approaches such as genetic algorithms and evolutionary strategies. The findings highlight growing trends in hybrid quantum–classical integration, the widespread adoption of metaheuristic optimization, and the importance of multi-objective frameworks adapted to quantum hardware constraints. Finally, the review identifies key research gaps and future directions for practical QCNN deployment on near-term quantum devices.
Quantum Neural Networks (QNNs) based on parameterized quantum circuits (PQCs) are gaining significant research attention due to their potential to achieve quantum advantages on Near-Term Noisy Intermediate-Scale Quantum (NISQ) computers. However, executing QNNs on NISQ devices is challenging due to quantum noise. To address this, we propose a noise-resilient QNN (NR-QNN) that leverages the unique characteristics of PQCs to perform noise-aware optimizations during the inference stage of QNNs. Specifically, NR-QNN employs two optimization techniques to mitigate the impact of noise on QNNs: quantum pruning and sensitivity-aware qubit mapping. The first technique is quantum pruning which identifies gates with small angle of rotations and removes them to simplify circuit of QNNs. The second optimization technique is sensitivity-aware qubit mapping which maps more important logical qubits to more reliable physical qubits. This technique is based on the observation that there can be variation in the sensitivity of an output to input qubits in a QNN. NR-QNN exploits this variability and guides qubit allocation to use reliable physical qubits for sensitive logical qubits. Our evaluation on a real quantum computer demonstrates that NR-QNN enhances the robustness of QNNs, enabling them to operate effectively on NISQ devices.