It is driven by proprietary quantum machine learning algorithms and methodologies and provides technology services to address complex AI optimization problems. Based on the Quantum Intelligence as a Service (QIaaS) hybridized platform architecture, QIT supports quantum computers, classical computers, and digital quantum simulators on the hardware layer. Current providers include D-Wave, AWS and AliCloud, etc. With QIT in place, business users will be able to conduct R&D and effectively build applications online through QIaaS. Inside QIT, they can interact directly with API layers for authentication, modeling and prediction, and evoke machine learning algorithms from classical models and Qindom's QI-enhanced ones.

Using data analytics to understand hospital emergency department overcrowding and wait times, two researchers have developed a methodology to predict future ER demand. Hospitals that use their analysis could use the results to reduce wait times for patients by as much as 15 percent, the researchers contend. The methodology uses machine learning technology to assess data on known patterns of ER activity, say Carri Chan, associate professor of business at Columbia Business School, and Kuang Xu, an assistant professor at Stanford Graduate School of Business. Their approach takes into account factors such as time of day, general level of severity, holidays, weather patterns, bad air quality, flu season and special events, to predict how many walk-in patients will come during a certain time period. That data then can help providers determine when to begin diverting them to their primary care physician, an urgent care facility or another hospital, as well as when to start diverting ambulances to other facilities.