Treffer: 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 years and is also increasingly exploited in several areas of physics, including quantum dynamics and control. The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of exploiting these powerful computational methods for the efficient implementation of modern quantum technologies. The existing frameworks for quantum system simulations, such as QuTip and QuantumOptics.jl, even though they are very successful in simulating quantum dynamics, cannot be easily incorporated into the platforms used for the development of machine learning models, like for example PyTorch. The TorchQC framework introduced in the present work comes exactly to fill this gap. It is a new library written entirely in Python and based on the PyTorch deep learning library. PyTorch and other deep learning frameworks are based on tensors, a structure that is also used in quantum mechanics. This is the common ground that TorchQC utilizes to combine quantum physics simulations and deep learning models. TorchQC exploits PyTorch and its tensor mechanism to represent quantum states and operators as tensors, while it also incorporates all the tools needed to simulate quantum system dynamics. All necessary operations are internal in the PyTorch library, thus TorchQC programs can be executed in GPUs, substantially reducing the simulation time. We believe that the proposed TorchQC library has the potential to accelerate the development of deep learning models directly incorporating quantum simulations, enabling the easier integration of these powerful techniques in modern quantum technologies. PROGRAM SUMMARY Program Title: TorchQC CPC Library link to program files: https://doi.org/10.17632/r8vtx3w4h9.1 Developer's repository link: https://github.com/qoptics-qtech/torchqc.git Licensing provisions: MIT Programming language: Python External libraries: PyTorch, NumPy, Matplotlib Nature of problem: Control of quantum systems is crucial for developing efficient algorithms that process quantum information and drive quantum systems to desired states. Machine Learning (ML) methods such as Deep Learning, Deep Reinforcement Learning, and basic or advanced optimization methods are increasingly used in Quantum Control by improving existing methodologies or even adding new ones. Simulation of quantum dynamics and Machine Learning methods are not available in a single framework, and one has to combine different libraries to develop a new method. Creating a custom algorithm by combining different libraries or frameworks is not always easy to do, and it requires a substantial amount of time and effort. Solution method: We propose a unified framework, called TorchQC, which exploits the tensor engine of the PyTorch library to create both quantum simulation methods and to easily embody ML methods in quantum dynamics and control. We believe that this framework would help users to easily implement ML methods ready to be applied in quantum systems. TorchQC provides all the necessary tools for simulating closed and open quantum systems, while it paves the way to incorporate ML methods into quantum control problems. Without the need to develop numerical simulation methods, the users will be able to produce quantum simulations from the framework's ready routines and directly use the simulated data in ML and optimization techniques. [ABSTRACT FROM AUTHOR]