  In this survey, we aim to explore the fundamental question of whether the
next generation of artificial intelligence requires quantum computing.
Artificial intelligence is increasingly playing a crucial role in many aspects
of our daily lives and is central to the fourth industrial revolution. It is
therefore imperative that artificial intelligence is reliable and trustworthy.
However, there are still many issues with reliability of artificial
intelligence, such as privacy, responsibility, safety, and security, in areas
such as autonomous driving, healthcare, robotics, and others. These problems
can have various causes, including insufficient data, biases, and robustness
problems, as well as fundamental issues such as computability problems on
digital hardware. The cause of these computability problems is rooted in the
fact that digital hardware is based on the computing model of the Turing
machine, which is inherently discrete. Notably, our findings demonstrate that
digital hardware is inherently constrained in solving problems about
optimization, deep learning, or differential equations. Therefore, these
limitations carry substantial implications for the field of artificial
intelligence, in particular for machine learning. Furthermore, although it is
well known that the quantum computer shows a quantum advantage for certain
classes of problems, our findings establish that some of these limitations
persist when employing quantum computing models based on the quantum circuit or
the quantum Turing machine paradigm. In contrast, analog computing models, such
as the Blum-Shub-Smale machine, exhibit the potential to surmount these
limitations.
