Quantum Machine Learning (QML) combines principles of quantum computing with traditional Machine Learning (ML) to explore computational advantages in data processing and model efficiency. With the rise of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum–classical approaches are gaining momentum, especially in domains requiring high precision such as healthcare. In this work, we investigate whether hybrid quantum computing can enhance certain aspects of classical ML, specifically in dataset balancing and the complexity of the neural network involved in training. To this end, we use the Indian Liver Patient Dataset as a case study to determine the presence of liver disease. We present the methodology for developing ‘QML-Liver’, a hybrid approach that seamlessly integrates classical and QML techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and F1-Score. Additionally, we successfully reduce the number of required qubits to just two, making practical deployment more feasible. These findings underscore the potential of QML for medical diagnostics, particularly in the NISQ era.

Abstract Quantum squaring circuits play a critical role in many quantum algorithms; however, most existing designs incur a significant qubit overhead due to the loss of input states and excessive use of ancillary qubits. In this work, we introduce a qubit-efficient quantum circuit for integer squaring that achieves a linear qubit cost of only 3 N qubits for an N -bit input, significantly outperforming state-of-the-art designs that scale quadratically in terms of qubits. Our approach reintegrates the input operand after computation, enabling the uncomputation of intermediate results and efficient recycling of ancilla qubits. This reversible strategy prevents the retention of redundant information, which is a common limitation of prior works. The comparative analysis confirms the scalability and practicality of our design for qubit-constrained quantum hardware, offering a promising solution for arithmetic operations in resource-limited quantum environments.

Abstract Adders are one of the most interesting circuits in quantum computing due to their use in major algorithms that benefit from the special characteristics of this type of computation. Among these algorithms, Shor’s algorithm stands out, which allows decomposing numbers in a time exponentially lower than the time needed to do it with classical computation. In this work, we propose three fault-tolerant carry lookahead adders that improve the cost in terms of quantum gates and qubits with respect to the rest of quantum circuits available in the literature. Their optimal implementation in a real quantum computer is also presented. Finally, the work ends with a rigorous comparison where the advantages and disadvantages of the proposed circuits against the rest of the circuits of the state of the art are exposed. Moreover, the information obtained from such a comparison is summarized in tables that allow a quick consultation to interested researchers.

Geospatial data follows Moore's law. On the back of improvements of optical Earth observation satellite hardware [1] (weight, propulsion systems, signal transmission and resolution) as well as reduced costs of rocket launch carrying these satellites, number of nanosatellites deployed to Lower Earth Orbit (LEO) in 2018 was larger than in the previous 10 years combined [2]. This allowed an exponential growth in satellite imagery data production that is available for military and commercial use. Machine learning tools enable us to process high resolution, multi-spectral satellite imagery data to recognize objects at scale [3] and generate insights with practical industry applications. It allowa us to calculate global oil reserves, track tanker ships or estimate retail revenue based on the car count, all exceptionally valuable financial information. In this paper, we investigate various computer vision techniques to develop an optimal machine learning technique for object recognition problems at this unique type of the dataset: multi-spectral satellite imagery.