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TorchQC -- A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

Machine learning has been revolutionizing our world over the last few yearsand is also increasingly exploited in several areas of physics, includingquantum dynamics and control.The need for a framework that brings togethermachine learning models and quantum simulation methods has been quite highwithin the quantum control field, with the ultimate goal of exploiting thesepowerful computational methods for the efficient implementation of modernquantum technologies. The existing frameworks for quantum system simulations,such as QuTip and QuantumOptics.jl, even though they are very successful insimulating quantum dynamics, cannot be easily incorporated into the platformsused for the development of machine learning models, like for example PyTorch.The TorchQC framework introduced in the present work comes exactly to fill thisgap. It is a new library written entirely in Python and based on the PyTorchdeep learning library. PyTorch and other deep learning frameworks are based ontensors, a structure that is also used in quantum mechanics. This is the commonground that TorchQC utilizes to combine quantum physics simulations and deeplearning models.TorchQC exploits PyTorch and its tensor mechanism to representquantum states and operators as tensors, while it also incorporates all thetools needed to simulate quantum system dynamics. All necessary operations areinternal in the PyTorch library, thus TorchQC programs can be executed in GPUs,substantially reducing the simulation time. We believe that the proposedTorchQC library has the potential to accelerate the development of deeplearning models directly incorporating quantum simulations, enabling the easierintegration of these powerful techniques in modern quantum technologies.