Bartkiewicz Karol, Tulewicz Patrycja, Roik Jan, Lemr Karel
Institute of Spintronics and Quantum Information, Adam Mickiewicz University, 61-614, Poznan, Poland.
Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17, Listopadu 12, 771 46, Olomouc, Czech Republic.
Sci Rep. 2023 Aug 9;13(1):12893. doi: 10.1038/s41598-023-40137-1.
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 maximally-entangled 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.
我们引入了一种全新的生成式量子机器学习方法，该方法显著减少了超参数的数量，并报告了一个证明我们方法可行性的原理验证实验。我们的提议依赖于生成器和判别器之间的协作，因此，我们将其称为量子协同生成学习。我们给出了数值证据，表明在某些情况下，这种协同方法优于最近提出的量子生成对抗学习。除了用量子模拟器获得的结果外，我们还展示了用实际可编程量子计算机获得的实验结果。我们研究了实现生成学习算法的量子计算机如何学习最大纠缠态的概念。在完成学习过程后，该网络既能识别又能生成纠缠态。我们的方法可被视为理解量子计算机如何学习和证明量子纠缠概念的一个可能的初步步骤。
