Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps
This work addresses mental health prediction in the tech sector, but it is incremental as it applies existing QSVM methods to a new dataset.
The paper tackled predicting future mental health treatment needs in the tech industry using a Quantum Support Vector Machine (QSVM) with non-classically simulable feature maps, achieving good performance models on OSMI survey data.
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.