Research in high-energy physics, as in many other scientific domains, makes extensive use of Monte Carlo calculations — both in theory and in experiments.

GANs are among the most interesting models in classical machine learning. GANs are an example of generative models (i.e. models that learn a hidden distribution from a training dataset) and can sample new synthetic data. We have been investigating their use as an alternative to Monte Carlo simulation, obtaining remarkable results. Much faster than standard Monte Carlo algorithms, GANs can generate realistic synthetic data, while retaining a high level of accuracy (see our fast simulation project). Quantum GANs could have more representational power than classical GANs, making them better able to learn more complex distributions from smaller training datasets. We are now training a quantum GAN to generate images of a few pixels. For this we are investigating two possible approaches:

• A hybrid schema with a quantum generator learning the target PDF, using either a classical network or a variational quantum circuit as a discriminator (variational quantum generator).
• A full quantum adversarial implementation (quGAN).

Monte Carlo methods make extensive use of random numbers. To ensure simulated data is unbiased, it is essential to understand the quality of the random number generators, the question of true randomness, and other related issues. Modern pseudo-random number generators exhibit optimal performance; we are studying the effect of replacing those with real quantum numbers in different Monte Carlo applications.