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Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ Quantum Computers for solving Quantum Machine Learning problems. The quantum advantage comes into picture due to the exponential speedup offered over classical computing. One of the major challenges in implementing such algorithms is the choice of quantum embeddings and the use of a functionally correct quantum variational circuit. In this paper, we present an application of QSVM (Quantum Support Vector Machines) to predict if a person will require mental health treatment in the tech world in the future using the dataset from OSMI Mental Health Tech Surveys. We achieve this with non-classically simulable feature maps and prove that NISQ HQC Architectures for Quantum Machine Learning can be used alternatively to create good performance models in near-term real-world applications.
Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more traditional architectures such as restricted Boltzmann machines (RBMs) an
Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology. Recent approaches for non-linear feature selection employing greedy optimisation of
In this paper, we develop a theory of learning nonlinear input-output maps with fading memory by dissipative quantum systems, as a quantum counterpart of the theory of approximating such maps using classical dynamical systems. The theory identifies t
Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successiv
Quantum computers have the opportunity to be transformative for a variety of computational tasks. Recently, there have been proposals to use the unsimulatably of large quantum devices to perform regression, classification, and other machine learning