Birds and fish do.Also bees, ants and termites. They form swarms and take advantage of the fact that they are stronger, smarter and faster in the group. But can such swarm intelligence also be transferred to quantum networks? A team led by Tanjung Krisnanda from Nanyang Technological University in Singapore asked itself this question and found out: Yes, several poorly trained quantum networks together are more powerful than a single well-trained one. It presents its results in the current issue of the journal»Physical Review Applied».
In the social sciences, swarm intelligence means that a decision made collectively by several people or a jointly achieved result is superior to the performance of a single expert. The principle is applied to economic forecasts, public policy decision-making, medical diagnostics and scientific advice. Based on this is the so-called»ensemble learning»in classical computer science.The point is that a combination of different learning algorithms can be better than one algorithm alone.
Now, when artificial neural networks contain quantum systems as nodes, they are called neural quantum networks. In this case, the researchers used a special quantum reservoir network (QRN) that exploits the nonlinear nature of quantum mechanical interactions. Each input influences the next reaction.
The input consists of two qubits that interact with the QRN for a certain period of time, so that the information about the input flows into the state of the QRN. By reading the QRN nodes and processing the result using a trained output layer, the scientists are able to completely reconstruct the input state (tomography) or detect whether the input qubits are entangled. «Assuming that the sum of the training runs is the same in both cases, we show that the collective result of the swarm estimates the input states better than the expert,» the authors note.This conclusion also applies to the task of entanglement detection.
In order to be able to compare experts and collectives, the scientists had imposed on themselves that the total number of training runs must be identical for both systems. However, if this restriction is loosened, they write, the enlargement of the swarm even offers the possibility of making the error rate arbitrarily low. This, in turn, is a crucial prerequisite for being able to reliably use machine learning in real applications. «We assume that our results can be generalized to many typical quantum architectures and tasks of machine learning with quanta.»
Source — https://www.spektrum.de/news/quantennetzwerke-zeigen-schwarmintelligenz/2116689