Richard P. Feynman once said: "Nature isn't classical and if you want to make a simulation of nature, you'd better make it quantum mechanical.”
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical comput- ers on machine learning tasks. The field of quantum machine learning explores how to devise and implement concrete quantum software that offers such advantages. Recent work has made clear that the hardware and software challenges are still considerable but has also opened paths towards solutions.
QML will advance artificial intelligence to an unimaginable extent. Businesses should be aware that all data-related problems will be solvable very soon.
Challenges in machine learning lend themselves particularly to quantum computing: "QC will play a critical role in the creation of artificial intelligence," says Geordie Rose, Founder of D-Wave, one of the first companies to build quantum computers. The MIT tech review agrees: "Quantum computers will be particularly suited to factoring large numbers […], solving complex optimization problems, and executing machine-learning algorithms. And there will be applications nobody has yet envisioned." Clearly, quantum machine learning (QML) is going to be the next big thing, disrupting the already mind-boggling field of artificial intelligence.
The key promise for business is that quantum computing will be able to extract the maximum meaning from Big Data. Generally, players are keeping relatively quiet about their achievements here. Tech giants such as Alibaba and Tencent are among those generating least noise. Startups such as IonQ, Quantum Circuits and RIKEN are also increasingly investing in the development of hardware. However, none of these players has shown their work publically yet.