Learning on sample-limited data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on a sample-limited data problem in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware. The ensembles include both variational and non-variational methods as well as introducing a new quantum ensemble cosine classifier with randomly sampled unitaries. Our ensemble designs use minimal trainable parameters but require long-range connections between qubits. We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma (RCC) patients with the task of predicting patient response to immunotherapy. From the performance observed in simulation and quantum hardware experiments using up to 56 qubits, we demonstrate how quantum embedding structure affects performance and discuss how to extract informative features and build models that can learn and generalize effectively. We also find that quantum ensemble cosine classifiers were effective in learning from few training data, and all quantum ensembles performed comparatively to classical ensembles while using significantly fewer learners. We confirmed this performance characteristic in a separate RCC validation cohort. We present these exploratory results in order to assist other researchers in the design of effective learning using ensembles, particularly for similarly size constrained problems. Incorporating quantum computing in these data constrained problems offers hope for a wide range of studies in healthcare and life sciences where biological samples are relatively scarce given the feature space to be explored.
Learning on sample-limited data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on a sample-limited data problem in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware. The ensembles include both variational and non-variational methods as well as introducing a new quantum ensemble cosine classifier with randomly sampled unitaries. Our ensemble designs use minimal trainable parameters but require long-range connections between qubits. We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma (RCC) patients with the task of predicting patient response to immunotherapy. From the performance observed in simulation and quantum hardware experiments using up to 56 qubits, we demonstrate how quantum embedding structure affects performance and discuss how to extract informative features and build models that can learn and generalize effectively. We also find that quantum ensemble cosine classifiers were effective in learning from few training data, and all quantum ensembles performed comparatively to classical ensembles while using significantly fewer learners. We confirmed this performance characteristic in a separate RCC validation cohort. We present these exploratory results in order to assist other researchers in the design of effective learning using ensembles, particularly for similarly size constrained problems. Incorporating quantum computing in these data constrained problems offers hope for a wide range of studies in healthcare and life sciences where biological samples are relatively scarce given the feature space to be explored.
In the current era, known as Noisy Intermediate-Scale Quantum (NISQ), encoding large amounts of data in the quantum devices is challenging and the impact of noise significantly affects the quality of the obtained results. A viable approach for the execution of quantum classification algorithms is the introduction of a well-known machine learning paradigm, namely, the ensemble methods. Indeed, the ensembles combine multiple internal classifiers, which are characterized by compact sizes due to the smaller data subsets used for training, to achieve more accurate and robust prediction performance. In this way, it is possible to reduce the qubit requirements with respect to a single larger classifier while achieving comparable or improved performance. In this work, we present an implementation and an extensive empirical evaluation of ensembles of quantum instance-based classifiers for binary classification, with the purpose of providing insights into their effectiveness, limitations, and potential for enhancing the performance of basic quantum models. In particular, three classical ensemble methods and three quantum instance-based classifiers have been taken into account here. Hence, the scheme that has been implemented (in Python) has a hybrid nature. The results (obtained on real-world datasets) have shown an accuracy advantage for the ensemble techniques with respect to the single quantum classifiers, and also an improvement in robustness. In fact, ensembles have proven effective not only in mitigating unsuitable data normalizations but also in reducing the impact of noise on quantum classifiers, enhancing their stability.
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that make them up but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain B transformations of the quantum state which encodes the training set in only log(B) operations. This implies exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.
Abstract A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and have a lower variance than the individual classifiers that make them up, but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain B transformations of the quantum state which encodes the training set in only log ( B ) operations. This implies an exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively to the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.
Abstract Hybrid quantum machine learning combines classical minimization with parameterized quantum circuits, enabling powerful learning models even on near-term quantum hardware. This work presents an evaluation of hybrid quantum machine learning classifiers using the data re-uploading framework across nine binary and multi-class classification tasks with varying geometric complexity. We compare three quantum circuit architectures, single-qubit, two-qubit, and two-qubit entangled, trained with three classical minimizers: COBYLA, Nelder-Mead, and SLSQP. Our experiments span training sizes from 1 to 200 samples and reveal that two-qubit classifiers, when paired with the SLSQP minimizer, achieve the highest overall test accuracy of 99.775% on structured tasks and strong performance (up to 89.78%) on complex multi-class pattern. Among minimizers, SLSQP consistently provides the best generalization, while entanglement improves accuracy in non-linear and fragmented tasks. By analyzing accuracy trends, generalization gaps, and confusion matrices, this study offers practical guidance for designing quantum classifiers that scale effectively and generalize well, providing a foundation for real-world Hybrid quantum machine learning applications.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Quantum-enhanced methods have recently attracted significant attention for addressing a wide range of machine learning problems. Many approaches exploit the hybrid resources of noisy quantum devices in combination with classical computing, particularly for supervised learning. Among these, the variational quantum classifier (VQC) offers potential quantum advantage by mapping classical data into high-dimensional quantum feature spaces that are difficult to simulate classically. While several quantum data-encoding methods have been explored, the efficient mapping of discrete attributes—such as postal codes, age groups, or categorical identifiers—has received limited attention, despite their importance in real-world classification tasks. In this work, we introduce an efficient encoding strategy based on Quantum Random Access Codes (QRACs) for embedding discrete features into compact quantum representations. QRACs enable multiple bits of information to be encoded into fewer qubits, thereby reducing resource requirements while preserving feature separability. We present several QRAC-based schemes, analyze their theoretical properties, and evaluate their performance through algebraic simulations. Empirical studies on real-world datasets, implemented on both simulators and quantum hardware, demonstrate that QRA-Cenhanced VQCs achieve faster training, reduced parameter counts, and competitive classification accuracy. These results highlight QRACs as a practical tool for advancing the applicability of quantum machine learning to structured datasets.