Abstract Fault-tolerant quantum neural networks (QNNs) require architectures that balance learning capability with stringent non-Clifford resource constraints. This work proposes an arithmetic-aware QNN architecture that integrates a validity-controlled binary-coded decimal (BCD)-to-Excess-3 transformation as a fault-tolerant preprocessing stage prior to variational learning. The arithmetic module is realized entirely within the Clifford+T gate set and incorporates explicit BCD validity detection, enabling conditional arithmetic execution while introducing only four additional T / T † gates beyond the underlying arithmetic core. The proposed architecture is combined with a COBYLA-optimized variational ansatz and compared against a baseline QNN of identical trainable complexity. While both models achieve identical classification accuracy of 100% on the benchmark single-digit task, the arithmetic-aware architecture demonstrates superior robustness under realistic hardware noise, achieving a robustness integral of 0.080 compared to 0.071 for the baseline model. Furthermore, curvature analysis reveals a substantially flatter optimization landscape, reducing the mean curvature from 36.85 to 27.21 and indicating that the arithmetic transformation acts as an effective inductive bias that stabilizes the learning process. To evaluate scalability, an additional four-class multi-digit (00–99) classification experiment was conducted, achieving an overall accuracy of 70%. These results demonstrate that fault-tolerant arithmetic preprocessing can enhance QNN robustness and stability while maintaining bounded non-Clifford overhead, highlighting the potential of arithmetic-aware representations for robust quantum machine learning.
Abstract Fault-tolerant quantum neural networks (QNNs) require architectures that balance learning capability with stringent non-Clifford resource constraints. This work proposes an arithmetic-aware QNN architecture that integrates a validity-controlled binary-coded decimal (BCD)-to-Excess-3 transformation as a fault-tolerant preprocessing stage prior to variational learning. The arithmetic module is realized entirely within the Clifford+T gate set and incorporates explicit BCD validity detection, enabling conditional arithmetic execution while introducing only four additional T / T † gates beyond the underlying arithmetic core. The proposed architecture is combined with a COBYLA-optimized variational ansatz and compared against a baseline QNN of identical trainable complexity. While both models achieve identical classification accuracy of 100% on the benchmark single-digit task, the arithmetic-aware architecture demonstrates superior robustness under realistic hardware noise, achieving a robustness integral of 0.080 compared to 0.071 for the baseline model. Furthermore, curvature analysis reveals a substantially flatter optimization landscape, reducing the mean curvature from 36.85 to 27.21 and indicating that the arithmetic transformation acts as an effective inductive bias that stabilizes the learning process. To evaluate scalability, an additional four-class multi-digit (00–99) classification experiment was conducted, achieving an overall accuracy of 70%. These results demonstrate that fault-tolerant arithmetic preprocessing can enhance QNN robustness and stability while maintaining bounded non-Clifford overhead, highlighting the potential of arithmetic-aware representations for robust quantum machine learning.
Quantum computing has the potential to transform modern computation by offering exponential advantages in areas such as cryptography, optimization, and intelligent data processing. To effectively realize these advantages, particularly in fault-tolerant and Noisy Intermediate-Scale Quantum (NISQ) environments, quantum circuits must be both resource-efficient and error-resilient. This paper presents a novel Binary-Coded Decimal (BCD) to Excess-3 code converter designed exclusively using the Clifford+T gate set, which is widely supported by fault-tolerant quantum hardware. The proposed design eliminates conventional 4-bit reversible adder-based implementations and instead employs an optimized logic structure based on Clifford+T-decomposed Peres gates. By leveraging Temporary Logical-AND gates and CNOT operations, the circuit achieves reduced T-count, circuit depth, and quantum cost as key metrics in fault-tolerant quantum computation. Functional correctness is verified through IBM Qiskit, Version 2.1 simulations for all valid BCD inputs. The proposed converter serves as a scalable and hardware-compatible arithmetic building block for resource-aware and AI-oriented quantum architectures.
We propose a hardware efficient quantum residual neural network which implements residual connections through a deterministic mixture of the identity operation and variational unitaries, enabling fully differentiable training. In contrast to the previous implementation of residual connections, our architecture avoids post-selection while preserving residual learning. Furthermore, we highlight circuit constructions where barren plateaus could be mitigated, which are considered as a major limitation of variational quantum learning models. In order to show the working of our model, we report its application to image classification tasks by training it for MNIST, CIFAR, and SARFish datasets, achieving accuracies of 99\% and 80\% for binary and multi-class classifications, respectively. These accuracies are comparable to previously achieved from the standard variational models, however our model requires 10x fewer gates making it better suited for resource constraint near-term quantum processors. In addition to high accuracies, the proposed architecture also demonstrates adversarial robustness which is another desirable parameter for quantum machine learning models. Overall our architecture offers a new pathway for developing accurate, robust, trainable and hardware efficient quantum machine learning models.
Quantum Arithmetic Logic Units (ALUs) serve as critical components in quantum processors, enabling both arithmetic and logical operations on qubits. However, the fragile nature of quantum systems makes them susceptible to errors and decoherence, necessitating fault-tolerant designs. In this work, we propose a fault-tolerant quantum ALU architecture constructed using the Clifford+ T gate set, which is widely recognized for its compatibility with error-correcting codes. The proposed design minimizes quantum resource overhead while maintaining computational accuracy under fault-prone conditions. Notably, the architecture requires zero ancilla and generates a single garbage output, optimizing the qubit space. Performance evaluations demonstrate significant improvements, achieving a 31.8% reduction in T-count and a 33.3% reduction in T-depth compared to existing designs. These enhancements contribute to more efficient and reliable quantum computation, paving the way for scalable quantum processor development.
Resource constraints and imperfect quantum operations remain major challenges for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices, particularly for fault-diagnosis tasks involving imbalanced and heterogeneous data. This work proposes QTN-RQNN, a Quantum Tensor Network-Based Recurrent Quantum Neural Network that integrates tensor-network-inspired nearest-neighbor interactions with recurrent quantum processing. The model sequentially encodes input features while propagating quantum memory across recurrent steps and incorporates QSMOTE for class balancing. QTN-RQNN is evaluated on four heterogeneous fault-diagnosis datasets SPID, CWRUBD, EFDD, and SFDD and compared with QNN, QTN-QNN, and RQNN baselines through classification, ablation, multi-seed, convergence, and quantum-resource analyses. Results show dataset-dependent predictive behavior, indicating that increased quantum architectural complexity does not universally improve classification. On SPID, QTN-RQNN improves the RQNN F1-score from 0.3077 to 0.4516 and ROC-AUC from 0.4485 to 0.6970, while the remaining datasets exhibit different performance-resource trade-offs and sensitivity to recurrent timesteps and circuit depth. Across all datasets, QTN-RQNN reduces the two-qubit-gate count from 32 to 24 per sample relative to RQNN, corresponding to a 25% reduction without increasing the four-qubit requirement; the same relative reduction is obtained for QTN-QNN over QNN. These findings establish QTN-RQNN as a resource-conscious recurrent quantum architecture and highlight the need to jointly consider predictive performance and circuit-resource requirements for near-term quantum fault diagnosis.
Quantum neural networks (QNNs) offer a principled pathway for integrating quantum computation with machine learning through superposition- and entanglement-based representations. This chapter proposes an architecture-aware design and evaluation framework for modern QNNs, emphasizing robustness and system feasibility alongside predictive performance. Multiple architecturesvariational QNNs, quantum convolutional neural networks, tensor-network hybrids, and fully quantum models—are assessed under a unified protocol. Experimental analysis shows that the proposed architecture-search–guided QNN achieves 91.8% classification accuracy and an F1-score of 0.914, outperforming fixed-template variational QNNs by approximately 5.6 percentage points. Under depolarizing noise with probability p = 0.10, the proposed model retains 85.3% accuracy, whereas baseline QNNs fall below 80%. Moreover, circuit depth is reduced by nearly 25% relative to standard variational designs, leading to faster convergence (42 epochs vs. 57 epochs).
Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum Circuits (VQCs) at their core and featuring layers of quantum gates optimized by classical algorithms, have garnered significant attention. However, a rigorous and exhaustive evaluation of their practical performance remains largely incomplete. In this study, we conduct a comprehensive comparative analysis of three prominent hybrid classical-quantum architectures: Quantum Convolutional Neural Networks (QCNN), Quantum Recurrent Neural Networks (QRNN), and Quantum Vision Transformers (QViT), focusing on the critical dimensions of generalization, accuracy, and robustness. Our findings provide novel insights that address previous evaluative gaps. Notably, while these models exhibit exceptional performance on low-feature datasets such as MNIST, their learning efficacy degrades significantly when transitioned to high-feature datasets. Furthermore, convolutional-based models like QCNN appear less effective on high-dimensional data than other machine learning architectures. Additionally, while all models are susceptible to adversarial noise, traditional architectures, such as recurrent and convolutional networks, demonstrate superior resilience. Conversely, in the presence of quantum noise, the transformer-based architecture proves its strength by maintaining high robustness against measurement noise, channel noise, and finite-shot effects, whereas other architectures suffer marked performance declines. These results provide a granular perspective on the current state of the field and underscore the critical importance of tailoring model selection to the constraints of contemporary Noisy Intermediate-Scale Quantum (NISQ) environments.