Hybrid Quantum Neural Networks (HQNNs), under the umbrella of Quantum Machine Learning (QML), have garnered significant attention due to their potential to enhance computational performance by integrating quantum layers within traditional neural network (NN) architectures. Despite numerous state-of-the-art applications, a fundamental question remains: Does the inclusion of quantum layers offer any computational advantage over purely classical models? If yes/no, how and why? In this paper, we analyze how classical and hybrid models adapt their architectural complexity in response to increasing problem complexity. To this end, we select a multiclass classification problem and perform comprehensive benchmarking of classical models for increasing problem complexity, identifying those that optimize both accuracy and computational efficiency to establish a robust baseline for comparison. These baseline models are then systematically compared with HQNNs by evaluating the rate of increase in floating-point operations (FLOPs) and number of parameters, providing insights into how architectural complexity scales with problem complexity in both classical and hybrid networks. We utilize classical machines to simulate the quantum layers in HQNNs, a common practice in Noisy Intermediate-Scale Quantum (NISQ) era. Our analysis reveals that, as problem complexity increases, the architectural complexity of HQNNs, and consequently their FLOPs consumption, despite the simulation overhead associated with quantum layer’s simulation on classical hardware, scales more efficiently $(53.1 \%$ increase in FLOPs from 10 features (low problem complexity) to 110 features (high problem complexity)), compared to classical networks (88.1%). Moreover, as the problem complexity increases, classical networks consistently exhibit a need for a larger number of parameters to accommodate the increasing problem complexity. Additionally, the rate of increase in number of parameters is also slower in HQNNs (81.4%) than classical NNs (88.5%). These findings suggest that HQNNs provide a more scalable and resource-efficient solution, positioning them as a promising alternative for tackling complex computational problems.
Hybrid Quantum Neural Networks (HQNNs), under the umbrella of Quantum Machine Learning (QML), have garnered significant attention due to their potential to enhance computational performance by integrating quantum layers within traditional neural network (NN) architectures. Despite numerous state-of-the-art applications, a fundamental question remains: Does the inclusion of quantum layers offer any computational advantage over purely classical models? If yes/no, how and why? In this paper, we analyze how classical and hybrid models adapt their architectural complexity in response to increasing problem complexity. To this end, we select a multiclass classification problem and perform comprehensive benchmarking of classical models for increasing problem complexity, identifying those that optimize both accuracy and computational efficiency to establish a robust baseline for comparison. These baseline models are then systematically compared with HQNNs by evaluating the rate of increase in floating-point operations (FLOPs) and number of parameters, providing insights into how architectural complexity scales with problem complexity in both classical and hybrid networks. We utilize classical machines to simulate the quantum layers in HQNNs, a common practice in Noisy Intermediate-Scale Quantum (NISQ) era. Our analysis reveals that, as problem complexity increases, the architectural complexity of HQNNs, and consequently their FLOPs consumption, despite the simulation overhead associated with quantum layer’s simulation on classical hardware, scales more efficiently (53.1% increase in FLOPs from 10 features (low problem complexity) to 110 features (high problem complexity)), compared to classical networks (88.1%). Moreover, as the problem complexity increases, classical networks consistently exhibit a need for a larger number of parameters to accommodate the increasing problem complexity. Additionally, the rate of increase in number of parameters is also slower in HQNNs (81.4%) than classical NNs (88.5%). These findings suggest that HQNNs provide a more scalable and resource-efficient solution, positioning them as a promising alternative for tackling complex computational problems.
Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with parameterized quantum circuits in an end-to-end trainable framework. However, their performance and efficiency depend strongly on architectural choices such as data encoding, circuit structure, measurement design, and the coupling between classical and quantum modules. This makes manual design increasingly difficult, especially when hardware limitations and resource constraints must also be taken into account. In this paper, we study the foundations of HQNNs and neural architecture search (NAS), discuss how NAS extends to quantum and hybrid settings, and demonstrate FLOPs-aware search (where FLOPs serve as a proxy for computational complexity), as an important hardware-aware direction for building HQNNs that are not only accurate but also computationally efficient and practically deployable.
Quantum machine learning (QML) is an emerging research area that combines quantum computation with classical machine learning (ML). The primary objective of QML is to enhance the performance of traditional ML algorithms by harnessing quantum phenomena. Inspired by the success of classical neural networks (NNs), their quantum analog, commonly known as quantum neural networks (QNNs), are widely being investigated. Despite the significant interest, the literature still lacks some concrete evidence about QNN's superiority over their classical counterparts, especially in practical applications. This paper empirically demonstrates a greater capacity in hybrid quantum neural networks (HQNNs) for a practical application, namely multi-class classification. In particular, we train both the HQNNs and their equivalent classical counterparts on the same data. We then benchmark the models' accuracy for quantifying model's capacity, where greater accuracy typically implies greater capacity. The results demonstrate a clear quantum advantage, i.e., greater capacity of HQNNs over their classical counterparts, where the HQNN models constantly achieve better accuracy. This superiority in performance by HQNNs serves as a foundational study for further investigation to magnify the quantum advantage in real-world applications of HQNNs.
Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution to these challenges. In particular, hybrid quantum-classical models can reduce the number of parameters while maintaining or improving performance compared to classical counterparts. Despite these advantages, challenges persist: why hybrid quantum models achieve these benefits, whether quantum neural networks (QNNs) can replace classical neural networks, and whether such models are feasible on noisy intermediate-scale quantum (NISQ) devices. This study addresses these challenges by proposing a hybrid quantum neural network (HQNN) that empirically demonstrates the capability to approximate non-linear functions in the latent feature space derived from classical embedding. The primary goal of this study is to achieve a parameter-efficient model in binding affinity prediction while ensuring feasibility on NISQ devices. Numerical results indicate that HQNN achieves comparable or superior performance and parameter efficiency compared to classical neural networks, underscoring its potential as a viable replacement. This study highlights the potential of hybrid QML in computational drug discovery, offering insights into its applicability and advantages in addressing the computational challenges of protein-ligand binding affinity prediction.
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.
This work focuses on the design and evaluation of Hybrid Quantum Neural Networks (HQNNs) that integrate the computational advantages of quantum circuits with the learning flexibility of classical deep neural networks to enhance machine learning performance in the Noisy Intermediate-Scale Quantum (NISQ) era.The study investigates two prominent HQNN architectures, namely Quantum Convolutional Neural Networks (QCNNs) and Quanvolutional Neural Networks (QuanNNs), with an emphasis on their robustness under realistic quantum noise conditions.It was evaluated using image classification tasks.Under various quantum noise channels, including Bit Flip, Phase Flip, Phase Damping, Amplitude Damping, and Depolarizing Noise, were introduced to reflect realistic hardware conditions.To reduce noise-induced performance loss, error mitigation techniques such as Zero-Noise Extrapolation, Probabilistic Error Cancellation, and Measurement Error Mitigation were applied.In addition, an AI-assisted adaptive framework is proposed to automatically recommend suitable mitigation strategies based on observed noise behavior and circuit characteristics.Experimental results indicate that QuanNN exhibits strong robustness under lowto-moderate noise levels and maintains stable performance even under high bit-flip noise, while QCNN demonstrates occasional performance gains under specific noisy configurations but degrades more rapidly as noise intensity increases.The proposed AI-guided mitigation mechanism further enhances model stability across varying noise regimes.This study provides practical insights into architecture selection, noise resilience, and intelligent error mitigation integration, offering a scalable framework for deploying HQNNs in real-world NISQ-era quantum machine learning applications.
Hybrid quantum-classical neural networks (HQNNs) integrate quantum circuits with classical layers, each operating under fundamentally different computational paradigm, which makes hardware-resource estimation challenging. Also, the training of quantum circuits on real devices requires thousands of circuit executions, which is impractical on current NISQ devices. Therefore, most HQNNs are evaluated on classical simulators, with hardware cost approximated using floating-point operations (FLOPs). However, FLOPs and existing quantum resource estimation methods (e.g., gate counts) overlook key quantum hardware-specific factors such as gate durations, limited qubit connectivity, and noise, all of which ultimately determine the true cost and scalability of quantum circuits.