Quantum Machine Learning for HEP Detector Simulations

Quantum Machine Learning (qML) is one of the most promising and very intuitive applications on near-term quantum devices which possess the potential to combat computing resource challenges faster than traditional computers. Classical Machine Learning (ML) is taking up a significant role in particle physics to speed up detector simulations. Generative Adversarial Networks (GANs) have proven to achieve a similar level of accuracy compared to Monte Carlo-based simulations while decreasing the computation time by orders of magnitude. In this research we are moving on and apply quantum computing to GAN-based detector simulations. Given the limitations of current quantum hardware in terms of number of qubits, connectivity, and noise, we perform initial tests with a simplified GAN model running on quantum simulators. The model is a classical-quantum hybrid ansatz. It consists of a quantum generator, defined as a parameterised circuit based on single and two qubit gates, combined with a classical discriminator. Our initial qGAN prototype focuses on a one-dimensional toy-distribution, representing the energy deposited in a detector by a single particle. It employs three qubits and achieves high physics accuracy thanks to hyper-parameter optimisation. Furthermore, we study the influence of real hardware noise for the qML GAN training. A second qGAN is developed to simulate 2D images with a 64-pixel resolution, representing the energy deposition patterns in the detector. Different quantum ansatzes are studied. We obtained the best results using a tree-tensor-network architecture with six qubits. Additionally, we discuss challenges and potential benefits of quantum computing as well as our plans for future developments.