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ARPN Journal of Engineering and Applied Sciences

NAVQNN: Noise-Aware adaptive variational quantum neural network framework for NISQ devices

Bommisetti Rakesh

Published Aug 20, 2026• 0 citations
Publisher

Abstract

The noise of quantum-enhanced learning there is a potential of quantum-enhanced learning with noisy intermediate-scale quantum (NISQ) devices, which is limited by hardware noise and finding useful circuit architectures. In this paper, a Noise-Aware Adaptive Variational Quantum Neural Network (NAVQNN) structure is proposed, which is capable of solving such issues simultaneously. The angle encoding of classical feature vectors (variance, skewness, kurtosis, entropy) of cheque images into quantum states is done on a shallow variational circuit. The noise-sensitive adaptive ansatz generator, in turn, develops the circuit layer-by-layer by picking the new parameterized gate blocks based on their validation loss with an explicit noise model and discouraging depth and two-qubit interactions in their cost functional. The ensuing variational quantum classifier is then trained by a hybrid quantum-classical optimization cycle based on the parameter-shift rule. The experiments on the UCI banknote authentication dataset indicate the usefulness of the proposed approach with the accuracy of 0.9913, precision of 0.9869, recall of 0.9934, F1-score of 0.9902, and ROC-AUC of 0.9998. Figures of prediction score densities, decision boundary, and live inference outputs are additional proof that NAVQNN can gain a strong and well-calibrated decision-making function in compliance with practical NISQ requirements. These findings suggest that noise-conscious adaptive variational circuits can be a useful foundation for quantum-enhanced financial fraud detection and various classification problems.

DOI
10.59018/062686

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The noise of quantum-enhanced learning there is a potential of quantum-enhanced learning with noisy intermediate-scale quantum (NISQ) devices, which is limited by hardware noise and finding useful circuit architectures. In this paper, a Noise-Aware Adaptive Variational Quantum Neural Network (NAVQNN) structure is proposed, which is capable of solving such issues simultaneously. The angle encoding of classical feature vectors (variance, skewness, kurtosis, entropy) of cheque images into quantum states is done on a shallow variational circuit. The noise-sensitive adaptive ansatz generator, in turn, develops the circuit layer-by-layer by picking the new parameterized gate blocks based on their validation loss with an explicit noise model and discouraging depth and two-qubit interactions in their cost functional. The ensuing variational quantum classifier is then trained by a hybrid quantum-classical optimization cycle based on the parameter-shift rule. The experiments on the UCI banknote authentication dataset indicate the usefulness of the proposed approach with the accuracy of 0.9913, precision of 0.9869, recall of 0.9934, F1-score of 0.9902, and ROC-AUC of 0.9998. Figures of prediction score densities, decision boundary, and live inference outputs are additional proof that NAVQNN can gain a strong and well-calibrated decision-making function in compliance with practical NISQ requirements. These findings suggest that noise-conscious adaptive variational circuits can be a useful foundation for quantum-enhanced financial fraud detection and various classification problems.
Noise and limited circuit depth remain major obstacles for deploying quantum machine learning models on noisy intermediate-scale quantum (NISQ) devices. Variational quantum neural networks (VQNNs) are promising for near-term learning, but their performance strongly depends on ansatz design and training stability under realistic noise. In this paper, we propose a Noise-Aware Adaptive Variational Quantum Neural Network (NAVQNN) for supervised binary classification. NAVQNN combines angle-based quantum encoding with a variational classifier whose circuit structure is grown adaptively from a candidate block pool using a noise-aware selection criterion that evaluates validation loss under a depolarizing noise model and penalizes hardware cost (depth and entangling gates). The model is trained in a hybrid quantum–classical loop using parameter-shift gradients and the Adam optimizer. Experiments on the UCI Banknote Authentication dataset demonstrate strong classification performance, achieving 99.13% accuracy and AUC = 0.9998 under NISQ-like noisy simulation, with consistently high precision, recall, and F1-score. These results indicate that noise-aware adaptive circuit construction can reduce dependence on hand-crafted ansätze while maintaining reliable performance in near-term quantum learning settings.
The hardware noise and drift, combined with the fragility of stationary hybrid algorithms like the variational quantum eigensolver (VQE), limit the usefulness of noise quantum advantage accessed through Noisy intermediate-scale quantum (NISQ) processors, but allow near-term quantum advantage. In this paper, a Hybrid Quantum-Classical Adaptive Intelligence Framework is suggested, which bridges the gap between quantum hardware, a parameterized quantum circuit, explicit error-mitigation routines, and a controller trained through learning. The quantum subsystem prepares variational states and calculates the expectation values of the problem Hamiltonian, and an AI/ML model approximates an online noise probability signal and moves an optimizer based on reinforcement learning (RL). The RL policy chooses the parameter updates and enables mitigation measures depending on noise-sensitive state functions, such as energy, gradient norm, and estimated noise level. It was demonstrated experimentally using a simulated NISQ-style molecular Hamiltonian that the adaptive controller can reduce the error in the absolute ground state of the system by about 52.8 percent versus a standard static VQE baseline using the same ansatz. The integrated classifier that drives the process of mitigation is accurate with 97 percent accuracy, 100 percent precision, 92 percent recall, 95.9 F1-score, and 99.5 AUC, which shows reliable decision-making with minimal false alarms. All these findings indicate that the close-knit quantum-classical adaptivity can significantly contribute to the robustness and accuracy of VQE-like workloads on NISQ and serve as a scalable template to design noise-aware hybrid algorithms in the future.
The execution of Variational Quantum Algorithms on Noisy Intermediate-Scale Quantum hardware is severely impacted by hardware noise variations over time and decoherence. This work introduces an Adaptive Noise-Aware Variational Quantum Classifier (ANAVQC) that integrates Zero-Noise Extrapolation (ZNE) dynamically into the training loop to counter variations. The framework approximates the instantaneous depolarizing noise from calibration and adapts noise scaling and linear extrapolation to estimate noise-free expectation values at training. A two-qubit parameterized quantum circuit is built and simulated in MATLAB, with the inclusion of stochastic gradient descent coupled with parameter-shift-based gradients. The model is conditioned on a synthetic binary classification data under a time-varying depolarizing noise profile and exhibits improved convergence stability and classification accuracy over non-adaptive methods. The simulation results identify that adaptive ZNE increases model robustness against time-varying noise and offers a scalable route to reliable quantum machine learning on practical NISQ hardware. The current ANAVQC scheme is extendable to multi-qubit platforms and practical quantum backends for the purposes of future experimental testing.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
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.
This paper presents Adaptive Quantum-Enhanced Learning (AQEL), a novel hybrid quantum-classical machine learning framework designed to address critical challenges in the Noisy Intermediate-Scale Quantum (NISQ) era. AQEL integrates four adaptive components: Adaptive Quantum Feature Mapping (AQFM), a Noise-Aware Variational Quantum Circuit (NAVQC), a Classical Meta-Optimizer with Quantum Feedback (CMOQF), and a Secure Scalable Data Interface (SSDI). Unlike static hybrid approaches, AQEL is designed to dynamically adapt its quantum architecture based on performance metrics and gradient behavior. We evaluate the framework's effectiveness on a benchmark binary classification task via high-fidelity simulation. The results demonstrate high classification accuracy, with the model automatically adapting its circuit depth during training to navigate the expressibility-trainability trade-off. Comprehensive ablation studies and a simulated noise resilience analysis validate the potential of AQEL's adaptive components, indicating its promise for developing robust QML applications.