  We introduce a new approach towards generative quantum machine learning
significantly reducing the number of hyperparameters and report on a
proof-of-principle experiment demonstrating our approach. Our proposal depends
on collaboration between the generators and discriminator, thus, we call it
quantum synergic generative learning. We present numerical evidence that the
synergic approach, in some cases, compares favorably to recently proposed
quantum generative adversarial learning. In addition to the results obtained
with quantum simulators, we also present experimental results obtained with an
actual programmable quantum computer. We investigate how a quantum computer
implementing generative learning algorithm could learn the concept of a Bell
state. After completing the learning process, the network is able both to
recognize and to generate an entangled state. Our approach can be treated as
one possible preliminary step to understanding how the concept of quantum
entanglement can be learned and demonstrated by a quantum computer.
