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IEEE International Conference on Quantum Software (QSW)

Noise-Aware Evaluation and Adaptive Optimization Level Selection for Quantum Circuits in NISQ Systems

Mahesh Babu Chittem, Varsha Sambhaje

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/qsw72780.2026.00019

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Noisy Intermediate-Scale Quantum (NISQ) devices are promising platforms for practical quantum computing, but their performance is significantly affected by limited qubit availability, gate errors, decoherence, circuit depth, and hardware-dependent noise. Conventional quantum circuits are generally designed and optimized before execution and may not respond effectively to changing hardware conditions during computation. This research proposes a Self-Adaptive Quantum Circuit framework that dynamically modifies quantum circuits according to the current computational resources and noise characteristics of NISQ devices. The proposed framework monitors parameters such as qubit fidelity, gate error rates, circuit depth, execution time, and resource availability, and automatically selects suitable circuit configurations to improve computational performance. Adaptive mechanisms can be employed to reduce unnecessary gates, optimize qubit utilization, and mitigate the impact of hardware noise while maintaining the required computational accuracy. The framework will be evaluated using simulation platforms and available NISQ quantum hardware, comparing adaptive circuits with conventional static circuits using metrics such as accuracy, fidelity, circuit depth, gate count, execution time, and error rate. The expected outcome is a resource-efficient and noise-aware quantum circuit optimization approach capable of improving the reliability and performance of quantum applications on near-term quantum hardware. The proposed research aims to contribute toward more practical, flexible, and hardware-aware quantum computing systems for NISQ-era applications. Keywords: NISQ Computing, Self-Adaptive Quantum Circuits, Quantum Circuit Optimization, Quantum Error Mitigation, Quantum Noise, Quantum Computing.
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.