In recent years, significant breakthroughs have been made in both artificial intelligence and quantum information science. The synergy between these domains has opened new frontiers for innovation, offering transformative potential across research and industry. A growing body of work has explored quantum-enhanced learning models, with many proposals validated through simulations on classical hardware emulating quantum systems. Yet, the absence of uniform evaluation standards makes it difficult to objectively assess the performance of these emerging techniques. This paper explores the applicability of quantum-augmented learning methods to tangible, albeit small-scale, problem-solving scenarios. Focusing on practical implementation, we empirically evaluate two hybrid approaches: support vector machines with quantum-inspired kernel methods and neural networks adapted for quantum processing. To ensure robust analysis, our experiments leverage five diverse datasets, each processed using multiple quantum state encoding schemes. Through systematic comparison, we aim to clarify the strengths and limitations of current quantum-assisted models in handling real-world data challenges.
In recent years, significant breakthroughs have been made in both artificial intelligence and quantum information science. The synergy between these domains has opened new frontiers for innovation, offering transformative potential across research and industry. A growing body of work has explored quantum-enhanced learning models, with many proposals validated through simulations on classical hardware emulating quantum systems. Yet, the absence of uniform evaluation standards makes it difficult to objectively assess the performance of these emerging techniques. This paper explores the applicability of quantum-augmented learning methods to tangible, albeit small-scale, problem-solving scenarios. Focusing on practical implementation, we empirically evaluate two hybrid approaches: support vector machines with quantum-inspired kernel methods and neural networks adapted for quantum processing. To ensure robust analysis, our experiments leverage five diverse datasets, each processed using multiple quantum state encoding schemes. Through systematic comparison, we aim to clarify the strengths and limitations of current quantum-assisted models in handling real-world data challenges.
Background Quantum support vector machines and quantum kernel methods have emerged as promising approaches within quantum machine learning, with the goal of leveraging quantum computing to enhance classification performance and computational efficiency. This review systematically surveys recent advances in QSVM and kernel‐based quantum classifiers, and analyzes their algorithmic frameworks, experimental implementations, and practical challenges. Methods We systematically examine QSVM approaches, including three schemes based on HHL algorithm, Hadamard test, and variational optimization, alongside multi‐class extension and quantum feature mapping. Result Findings indicate that QSVM can offer theoretical speed‐ups in specific settings, particularly when combined with quantum feature mappings that encode data into high‐dimensional Hilbert spaces. However, current implementations remain constrained by hardware limitations, and a lack of large‐scale general validation. Most studies focus on proof‐of‐principle experiments with limited real‐world applicability. Conclusion While promising, QSVM requires further work on scalability, noise resilience, and real‐world integration. Future research should focus on robust algorithms and empirical studies.
Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
The integration of neuro-inspired algorithms and quantum computing in machine learning presents a promising frontier for addressing complex computational challenges in modern AI. Neuro-inspired algorithms, such as artificial neural networks (ANNs), deep learning (DL), and spiking neural networks (SNNs), have demonstrated impressive performance in various domains, including image recognition, natural language processing, and autonomous systems. This research explores the synergy between neuro-inspired algorithms and quantum computing, focusing on how quantum-enhanced machine learning models can accelerate training and inference processes in neuro-inspired systems. Quantum neural networks (QNNs) leverage quantum principles, such as superposition and entanglement, to represent and manipulate data in ways that classical systems cannot. By combining quantum computing's parallelism with the flexibility and learning capability of neuro-inspired algorithms, the proposed hybrid models can provide exponential speedups in tasks involving large-scale data processing and optimization. To evaluate the performance of these hybrid models, experiments were conducted using a quantum-enhanced deep learning model applied to image classification and a neuro-inspired algorithm augmented by quantum optimization techniques for optimization tasks. The quantum-enhanced deep learning model achieved a 45% reduction in training time compared to classical deep learning models while maintaining similar accuracy levels. These findings highlight the significant potential of combining quantum computing with neuro-inspired algorithms, opening new avenues for faster, more efficient machine learning models capable of solving previously unsolvable problems. The synergy between these two domains could lead to breakthroughs in areas like artificial general intelligence (AGI), drug discovery, and autonomous systems, where large-scale optimization and pattern recognition are critical.
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective.
Support vector machines are powerful and frequently used machine learning methods for classification and regression tasks, which rely on the construction of kernel matrices. While crucial for the performance of this machine learning approach, choosing the most suitable kernel is highly problem-dependent. The emergence of quantum computers and quantum machine learning techniques provides new possibilities for generating powerful quantum kernels. Within this work, we solve a real-world price forecasting problem using fidelity and projected quantum kernels, which are promising candidates for the utility of near-term quantum computing. In our analysis, we examine and validate the most auspicious quantum kernels from literature and compare their performance with an optimized classical kernel. Unlike previous work on quantum support vector machines, our dataset includes categorical features that need to be encoded as numerical features, which we realize by using the one-hot-encoding scheme. One-hot-encoding, however, increases the dimensionality of the dataset significantly, which collides with the current limitations of noisy intermediate scale quantum computers. To overcome these limitations, we use autoencoders to learn a low-dimensional representation of the feature space that still maintains the most important information of the original data. To examine the impact of autoencoding, we compare the results of the encoded date with the results of the original, unencoded dataset. We could demonstrate that quantum kernels are comparable to or even better than the classical support vector machine kernels regarding the mean absolute percentage error scores for both encoded and unencoded datasets.