Synergic quantum generative machine learning

We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparametersand report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration betweenthe generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence thatthe 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 actualprogrammable quantum computer. We investigate how a quantum computer implementing generative learning algorithm couldlearn the concept of a maximally- entangled state. After completing the learning process, the network is able both to recognizeand to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how theconcept of quantum entanglement can be learned and demonstrated by a quantum computer.