Well, it turns out to be very difficult to design algorithms that exploit the potential power of quantum computing. If, instead, we build a neural network, it can learn, in much the same way that we ourselves learn, not by following a series of logical instructions, but by experience. Instead of a very complicated central processing unit, as in your laptop, we have a very large number of very simple processors—neurons-- that are multiply interconnected. This bypasses the problem of algorithm construction, because the network learns how to do it.[note: the two different types of hyphens are in the original npr web page!]
Perhaps, in time, even creativity and consciousness.
Elizabeth is a professor in the physics department of Wichita State University.---wikipeida: it doesn't seem to offer PhD in physics...(but does in applied mathematics..but still..) See also https://en.wikipedia.org/wiki/Quantum_neural_network but this section fro that article clarifies:
Quantum networks: Some contributions reverse the approach and try to exploit the insights from neural network research in order to obtain powerful applications for quantum computing, such as quantum algorithmic design supported by machine learning. An example is the work of Elizabeth Behrman and Jim Steck, who propose a quantum computing setup that consists of a number of qubits with tunable mutual interactions. Following the classical backpropagation rule, the strength of the interactions are learned from a training set of desired input-output relations, and the quantum network thus ‘learns’ an algorithm. J. Bang et al. : A strategy for quantum algorithm design assisted by machine learning, New Journal of Physics 16 073017 (2014) (Journal is: "an online-only, open-access, peer-reviewed scientific journal covering research in all aspects of physics, as well as interdisciplinary topics where physics forms the central theme. The journal was established in 1998 and is a joint publication of the Institute of Physics and the Deutsche Physikalische Gesellschaft. ")
"The progress in machine learning critically relies on processing power," Dunjko, a physicist at the University of Innsbruck in Austria, told Phys.org. "Moreover, the type of underlying information processing that many aspects of machine learning rely upon is particularly amenable to quantum enhancements. As quantum technologies emerge, quantum machine learning will play an instrumental role in our society—including deepening our understanding of climate change, assisting in the development of new medicine and therapies, and also in settings relying on learning through interaction, which is vital in automated cars and smart factories"
In the new study, the researchers' main result is that quantum effects can help improve reinforcement learning, which is one of the three main branches of machine learning. They showed that quantum effects have the potential to provide quadratic improvements in learning efficiency, as well as exponential improvements in performance for short periods of time when compared to classical techniques for a wide class of learning problems.
While other research groups have previously shown that quantum effects can offer improvements for the other two main branches of machine learning (supervised and unsupervised learning), reinforcement learning has not been as widely investigated from a quantum perspective.
"This is, to our knowledge, the first work which shows that quantum improvements are possible in more general, interactive learning tasks," Dunjko said. "Thus, it opens up a new frontier of research in quantum machine learning."
One of the ways that quantum effects may improve machine learning is quantum superposition, which allows a machine to perform many steps simultaneously, improving the speed and efficiency at which it learns.
But while in certain situations quantum effects have the potential to offer great improvements, in other cases classical machine learning likely performs just as well or better than it would with quantum effects. Part of the reason for the difficulty in determining how quantum effects can improve machine learning is due to the unique set of challenges involved, beginning with the basic question of what it means to learn. Such a question becomes problematic, the scientists explain, since the machine and its environment may become entangled, blurring the boundary between the two.
Overall, the researchers expect that the systematic approach proposed here, which encompasses all three of the main branches of machine learning, will lead to the first steps in a complete theory of quantum-enhanced learning.