  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.
