Near-term quantum-classical associative adversarial networks

被引:7
|
作者
Anschuetz, Eric R. [1 ]
Zanoci, Cristian [1 ]
机构
[1] MIT, Dept Phys, 77 Massachusetts Ave, Cambridge, MA 02139 USA
关键词
SUPREMACY;
D O I
10.1103/PhysRevA.100.052327
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We introduce a hybrid quantum-classical adversarial machine learning architecture called a quantum-classical associative adversarial network (QAAN). This architecture consists of a classical generative adversarial network with a small auxiliary quantum Boltzmann machine that is simultaneously trained on an intermediate layer of the discriminator of the generative network. We numerically study the performance of QAANs compared to their classical counterparts on the MNIST and CIFAR-10 data sets, and show that QAANs attain a higher quality of learning when evaluated using the Inception score and the Frechet Inception distance. As the QAAN architecture only relies on sampling simple local observables of a small quantum Boltzmann machine, this model is particularly amenable for implementation on the current and next generations of quantum devices.
引用
收藏
页数:11
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