Characterization of entanglement using a quantum generative adversarial network

Machine learning can be used as a systematic method to non-algorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that difficult step and potentially reducing unnecessary complexity. In addition, the machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction.Here we apply our prior work in quantum machine learning technique, to create a QGAN, a quantum analog to the classical Stylenet GANs developed by Kerras for image generation and classification, and apply it to the problem of finding the best possible set of hyperplanes in the Hilbert space to separate out the subspace (known to be convex) of unentangled (product) states. Prelininary results show good training of both generator and discriminator.