Quantum computing is no longer a lab curiosity for academic research. Industrial processors exceeding 100 qubits are commercially accessible and, for the first time, can extract information from data in ways that classical algorithms struggle to match. The most direct way to monetize this capability for industrial production today is quantum feature extraction: turning raw business data (images, customer records, molecules, or sensor readings) into richer representations that outperform standard machine learning models. There is one obstacle, however, that stands between today's demonstrations and tomorrow's production systems: every sample of data costs a quantum computing execution. For a company with millions of customers, satellite images, or transactions per month, processing every sample on quantum hardware is simply not viable. This work introduces quantum feature surrogates, a framework developed by Kipu Quantum that breaks this bottleneck. The idea is intuitive though challenging: instead of asking the quantum computer to look at every single sample, we let it look at a small, carefully chosen subsample of the data, whose distribution faithfully represents the full set. A simple classical model, a surrogate, then learns the quantum-induced patterns and applies them to the rest of the dataset at near-zero cost. The quantum processor stops being a per-sample engine and becomes a teacher of representations, while production inference runs entirely on classical hardware.
Quantum computing is no longer a lab curiosity for academic research. Industrial processors exceeding 100 qubits are commercially accessible and, for the first time, can extract information from data in ways that classical algorithms struggle to match. The most direct way to monetize this capability for industrial production today is quantum feature extraction: turning raw business data (images, customer records, molecules, or sensor readings) into richer representations that outperform standard machine learning models. There is one obstacle, however, that stands between today's demonstrations and tomorrow's production systems: every sample of data costs a quantum computing execution. For a company with millions of customers, satellite images, or transactions per month, processing every sample on quantum hardware is simply not viable. This work introduces quantum feature surrogates, a framework developed by Kipu Quantum that breaks this bottleneck. The idea is intuitive though challenging: instead of asking the quantum computer to look at every single sample, we let it look at a small, carefully chosen subsample of the data, whose distribution faithfully represents the full set. A simple classical model, a surrogate, then learns the quantum-induced patterns and applies them to the rest of the dataset at near-zero cost. The quantum processor stops being a per-sample engine and becomes a teacher of representations, while production inference runs entirely on classical hardware.
This paper explores a novel approach to feature extraction and representation learning within the context of quantum machine learning. The core claim is to enhance the performance and efficiency of machine learning algorithms by leveraging quantum computation for these foundational tasks. We introduce a methodology that utilizes quantum algorithms to transform raw data into representations optimized for subsequent machine learning processes. This represents a new direction in the burgeoning field of quantum machine learning, offering potential advantages over classical methods, particularly for complex datasets and computationally intensive tasks. The paper details the theoretical framework, outlines the key components of the proposed system, and discusses potential applications and future research directions. The primary focus is on the algorithmic design and mathematical foundations underpinning this approach, aiming to provide a solid basis for future development and experimentation.
Abstract Quantum machine learning (QML) presents potential for early industrial adoption, yet limited access to quantum hardware remains a significant bottleneck for deployment of QML solutions. This work explores the use of classical surrogates to bypass this restriction, which is a technique that allows to build a lightweight classical representation of a (trained) quantum model, enabling to perform inference on entirely classical devices. We reveal prohibitive high computational demand associated with previously proposed methods for generating classical surrogates from quantum models, and propose an alternative pipeline enabling generation of classical surrogates at a larger scale than was previously possible. Previous methods required at least a high-performance computing (HPC) system for quantum models of below industrial scale (ca. 20 qubits), which raises questions about its practicality. We greatly minimize the redundancies of the previous approach, utilizing only a minute fraction of the resources previously needed. We demonstrate the effectiveness of our method on a real-world energy demand forecasting problem, conducting rigorous testing of performance and computation demand in both simulations and on quantum hardware. Our results indicate that our method achieves high accuracy on the testing dataset while its computational resource requirements scale polynomially rather than exponentially. This work presents a lightweight approach to transform quantum solutions into classically deployable versions, facilitating faster integration of quantum technology in industrial settings. Furthermore, it can serve as a powerful research tool in search of a practical quantum advantage in an empirical setup.
The advent of Noisy Intermediate-Scale Quantum (NISQ) machines has brought new opportunities for expanding machine learning abilities with the help of quantum computing. Herein, a complete hybrid quantum-classical learning system is introduced that incorporates classical preprocessing, quantum feature extraction, and classical machine learning in a strategic way to surpass the performance of existing systems for the tasks of complex pattern recognition. We perform large-scale experiments with two popular benchmark datasets: the Iris flower dataset and the Wisconsin Breast Cancer Diagnostic dataset. Our framework shows consistently improving accuracy of2−7%over stark classical methods without sacrificing practicality on state-of-the-art NISQ hardware. With rigorous ablation studies and cross-validation with cutting-edge approaches, we conclude that the best task distribution between traditional and quantum processors is key to near-term quantum advantage. Our introduced framework effectively achieves a quantum circuit depth reduction of 40 % using smart classical preprocessing along with improving classification performance using quantum-augmented feature representation. Our implementation offers both hardware-based execution with Qiskit as well as fast classical simulation modes, so it is available for researchers with limited quantum computing capabilities.
The contemporary manufacturing landscape is more complicated, data-heavy and more automated than ever before, producing enormous volumes of multi-modal sensor data, production logs and maintenance records. Predictive analytics within these environments becomes essential to achieve operational efficiency, reduce downtime, and optimise resource allocation; however, many classical machine learning methods struggle with high-dimensional, nonlinear, and dynamic data, resulting in suboptimal predictions and slow decision-making. To overcome these issues, the proposed research suggests a Quantum-Enhanced AI (QAI) system combining various quantum algorithms: Quantum Neural Networks (Quantum Neural Network) to recognize patterns, Quantum Supporter Vector Machines (Quantum Supporter Vector Machines) to classify, Quantum Approximate Optimization Algorithm (Quantum Approximate Optimization Algorithm) to schedule production and allocate resources, and Variational Quantum Eigensolver (Variational Quantum Eigensolver) to optimize parameters under noisy settings or conditions into a hybrid quantum-class The framework is a quantum computer based framework that encodes classical manufacturing data in the form of quantum states, using amplitude and qubit-efficient embeddings, allowing representation of high-dimensional features with the parallelism and entanglement of quantum computing. Realworld experimental validation of both turbofan engine degradation and semiconductor manufacturing sensor logs was conducted using IBM Qiskit and PennyLane to ensure reproducibility, with noise-aware training and error mitigation policies applied in simulations on NISQ-era hardware. The proposed QAI framework demonstrated higher predictive accuracy (12 to 15) than state-of-the-art hybrid quantum-classical and classical models, with superior accuracy, memorability, and F1 scores, and showed exceptional capabilities in anomaly detection, failure prediction, and optimisation. The results validate that quantum algorithms can be integrated into a predictive analytics pipeline to overcome classical computation bottlenecks, enhance robustness, and provide actionable insights for making real-time manufacturing decisions. Overall, this paper has demonstrated that quantum-assisted AI can be beneficial in intelligent manufacturing environments, laying the groundwork for expanding practical applications that bridge the gap between the promises of quantum computational power and predictive analytics in the industry.
In order to leverage quantum computers for machine learning tasks such as image classification, careful consideration is required: NISQ-era quantum computers have limitations, which include noise, scalability, read-in and read-out times, and gate operation times. Therefore, strategies should be devised to mitigate the impact that complex datasets can have on the overall efficiency of a quantum machine learning pipeline. This may otherwise lead to excessive resource demands or increased noise. We apply a classical feature extraction method using a ResNet10-inspired convolutional autoencoder to both reduce the dimensionality of the dataset and extract abstract and meaningful features before feeding them into a quantum machine learning block. The quantum block of choice is a quantum-enhanced support vector machine (QSVM), as support vector machines typically do not require large sample sizes to identify patterns in data and have short-depth quantum circuits, which limits the impact of noise. The autoencoder is trained to extract meaningful features through image reconstruction, aiming to minimize the mean squared error across a training set. Three image datasets are used to illustrate the pipeline: HTRU-1, MNIST, and CIFAR-10. We also include a quantum-enhanced one-class support vector machine (QOCSVM) for the highly unbalanced HTRU-1 set, as well as classical machine learning results to serve as a benchmark. Finally, the HTRU-2 dataset is also included to serve as a benchmark for a dataset with well-correlated features. The autoencoder achieved near-perfect reconstruction and high classification accuracy for MNIST, while CIFAR-10 showed poorer performance due to image complexity, and HTRU-1 struggled because of dataset imbalance. This highlights the need for a balance between dimensionality reduction through classical feature extraction and prediction performance using quantum methods.
Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and under noiseless conditions, coherent processing of quantum data outperforms fixed-measurement schemes followed by classical processing. It remained uncertain whether this performance gap persists at a finite scale, and in the presence of noise that is unavoidable with current quantum devices. In this work, we present simulations and analysis of the performance of existing hardware on a learning problem known to exhibit asymptotic advantage, now subjected to noisy quantum data. Comparing coherent quantum processing directly against fixed-measurement schemes, our results demonstrate a clear performance separation at a scale of just 30 to 40 noisy qubits. Already at this scale, the fundamental bottleneck is no longer classical computation but data acquisition; matching the noisy coherent protocol with measure-first strategies would still require months or even years of measurements. By systematically evaluating hardware constraints such as state preparation, gate errors, readout errors, connectivity, and coherence times, we provide evidence that a demonstration of such a strong learning advantage is accessible on near-term devices.