Unsupervised quantum circuit learning in high energy physics

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy using quantum circuit-based to generate synthetic data high energy physics processes. We use non-adversarial, gradient-based circuit Born machines joint distributions over 2 and 3 variables.