The use of quantum algorithms in artificial intelligence techniques will boost machines’ learning abilities. This will lead to improvements in the development, among others, of predication systems, including those of the financial industry. However, we’ll have to wait to start these improvements being rolled out.
The processing power required to extract value from the unmanageable swaths of data currently being collected, and especially to apply artificial intelligence techniques such as machine learning, keeps increasing. Researchers have been trying to figure out a way to expedite these processes applying quantum computing algorithms to artificial intelligence techniques, giving rise in the process to a new discipline that’s been dubbed Quantum Machine Learning (QML).
“Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers,” says Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA’s New Digital Businesses area. “We still have to find out to what extent do these models appear in practical applications.”
Machine learning and artificial intelligence technologies are the two key areas of research in the application of quantum computing algorithms. One of the particularities of this calculation system is that it allows representing several states at the same time, which is particularly convenient when using AI techniques. For example, as noted by Intel, voice-assistants could greatly from this implementation, as quantum could exponentially help improve their accuracy, boosting both their processing power and the amount of data they would be able to handle. Quantum computing increases the number of calculation variables machines can juggle and therefore allow them to provide faster answers, much like a person would.
More accurate algorithms
The ability to represent and handle so many states makes quantum computing extremely adequate for solving problems in a variety of fields. Intel has opened several lines of research on quantum algorithms. The first applications they are going to see are in fields such as material sciences, where the modeling of small molecules is a computing intensive task. Going forward, larger machines will allow designing medicines or optimizing logistics to, for example, find the most efficient route among any number of alternatives.