Description of the project:
Our main aim is to cater to the needs of people with autism by providing a quantum approach for early diagnosis of autism and to allocate resources efficiently amongst people. 
Diagnosis of ASD is still reliant on subjective evaluation by highly trained clinical experts. Psychiatric expertise is in short supply globally, with the problem being more acute in the developing world. This creates a situation where access to accurate, reliable and timely diagnosis for autism and other mental health disorders.
We propose a multi-pronged approach towards improving the state of care for autism patients across multiple levels of diagnostics and treatment accessibility. Specifically, we propose an efficient hybrid-classical quantum machine learning approach towards improving automated diagnosis, and make a parallel solution using QUBO to optimize placements of specialized treatment centres in a health system. The former is for clinicians and patients to be able to diagnose and take control of the condition in an expedited and efficient manner, closing the expertise gap. The latter is intended to guide policy measures for governments and administrative bodies towards enabling adequate access to mental healthcare facilities.
We dedicate the project to people with autism. We believe we can make a big difference to the lives of children with autism by helping them get diagnosed early. We advocate for an inclusion of children with autism to help them and their families to not feel excluded from the community.