Serving as managing director of The Robotics Hub, Chris Moehle guides a Pittsburgh-based company focused on funding advancements in the area of robotics. One area in which The Robotics Hub’s portfolio companies have a strong interest in (as a way of creating robots with superior decision-making capacities) is the intersection between machine learning and quantum computing.

Areas of emphasis within this hybrid field include the use of nascent quantum computers to expedite machine learning algorithms, with the ultimate goal being to employ the smallest possible computing system in interpreting and understanding large-scale datasets.

This area of research has its impetus in HHL, a quantum algorithm developed in 2008 that has the ability to find answers to vast linear algebra problems with many degrees of freedom at a potentially faster speed than traditional supercomputers. A key advantage of HHL over standard machine learning algorithms is that such a system can generate purely random numbers. One limitation is that quantum machine learning algorithms have thus far been designed as “frameworks for algorithms” instead of posing a classical problem that needs to be solved with a logically derived answer.

In sum, this provides a new way to solve problems. In areas where there is no classical solution – like areas of materials science and medicine – research, development, and implementation can be somewhat straightforward. However, in areas where classical computing is currently performing – such as navigation and optimization – research, development, and implementation must be somewhat more nuanced. An ideal solution allows quantum to be “layered” with traditional approaches allowing the stability of classical algorithms to coexist with the enhanced functions quantum promises.