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.
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 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.
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.
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.
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.
Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research has explored a higher level of optimization by making the quantum circuits themselves resilient to noise.In this paper, we propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search from parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates (e.g., U3 and CU3) and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates in a fine-grained manner.Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines. For QML tasks, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real quantum computers. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2compared with UCCSD baselines. We also open-source the TorchQuantum library for fast training of parameterized quantum circuits to facilitate future research.
This study investigates classification performance and trainability of variational quantum classifiers on near-term noisy quantum hardware across circuit depth and qubit count regimes within the context of quantum computing and machine learning, an area of growing scientific importance given its implications for hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment. Using gradient-based parameter optimization of parameterized quantum circuits with hardware noise modeling via density matrix simulation and physical execution on IBM Eagle processors, we examine expressibility of parameterized circuits enabling nonlinear decision boundaries in feature Hilbert space, with trainability constrained by barren plateau gradients at large qubit counts in 96 circuit architectures (4 qubit counts x 4 depths x 6 ansatz families) evaluated across 5 classification datasets with 20 random initialization seeds each drawn from IBM Quantum Eagle (127-qubit) and Falcon (27-qubit) processors with noise mitigation via zero-noise extrapolation. Results indicate that 10-qubit, 4-layer VQC achieves 84.2% classification accuracy on the best benchmark, within 3.8 percentage points of classical SVM, but gradient variance decays exponentially with qubit count (variance halving per 2 added qubits), confirming barren plateau scaling (p < 0.001), with 84.2% accuracy, 3.8 pp gap to classical SVM baseline as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to quantum computing and machine learning and carry actionable implications for the design of programs and policies targeting hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment.