Synergic quantum generative machine learning

TL;DR

This paper introduces a quantum synergic generative learning approach that reduces hyperparameters and demonstrates its effectiveness through simulations and real quantum computer experiments, including learning entangled states.

Contribution

It proposes a collaborative quantum generative learning method that outperforms some existing approaches and provides experimental validation on actual quantum hardware.

Findings

The approach reduces hyperparameters compared to previous methods.

Experimental results show successful learning of Bell states.

Numerical evidence suggests favorable comparison to quantum GANs.

Abstract

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.