The primary application of the computing method is drug discovery. Developing new drugs is of importance, including the current fight against COVID-19. Drug discovery is a commonly cited combinatorial optimization problem. The search for effective drugs involves an enormous number of potential matches between medically appropriate molecules and target proteins that are responsible for a specific disease.
Conventional computers are used to replicate chemical interactions in the medical space and other areas of life and chemical sciences. To really move forwards, quantum technology is required to take developments beyond trial and error to rapidly tackle the sheer volume of total possible combinations.
Other applications of the technology include:
One classic problem is that of the traveling salesman (a common logic problem) – identifying the shortest possible route that visits each of “n” number of cities, while returning to the city of origin. This problem and its variants appear in contemporary form in logistical challenges, such as daily automotive traffic patterns. The advantage of using a quantum information system is speed.
A CIM is also a good match for some types of machine learning, including image and speech recognition. Artificial neural networks “learn” by iteratively processing examples containing known inputs and results. CIMs can speed up the training and improve upon the accuracy of existing neural networks.
The development of the new computer system has been pioneered by Kazuhiro Gomi, CEO of NTT Research, and Dr. Yoshihisa Yamamoto, Director of NTT Research’s Physics & Informatics (PHI) Lab, who is overseeing this research. This is a step forwards in CIM optimization problems by uniting perspectives from statistics, computer science, statistical physics and quantum optics.
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