The simulation of driven dissipative quantum dynamics is often prohibitively computationintensive, especially when it is calculated for various shapes of the driving field. We engineer a new feature space for representing the field and demonstrate that a deep neural network can be trained to emulate these dynamics by mapping this representation directly to the target observables. We demonstrate that with this approach, the system response can be retrieved many orders of magnitude faster. We verify the validity of our approach using the example of finite transverse Ising model irradiated with few-cycle magnetic pulses interacting with a Markovian environment.We show that our approach is sufficiently generalizable and robust to reproduce responses to pulses outside the training set.
I. INTRODUCTIONThe simulation of dissipative quantum dynamics [1] is a problem which arises in diverse areas of physics, including ultrafast spectroscopy, chemical physics, quantum optics, quantum biology, quantum computing, and quantum information technology [2-6]. Some problems involving open quantum systems in the presence of a driving field, which include optimal control of open quantum systems [7, 8] and simulating coherent pulse propagation using the Maxwell-Schrödinger equations [9] when accounting for divergence-and propagation-induced decoherence [10-12], require simulating the system iteratively for different waveforms of the field. Many methods for simulating open quantum systems are available, including Monte Carlo methods [13], time evolving density matrix methods, and path integral approaches.However, they often become unsuitable for iterative methods by making any computations involving them undesirably expensive, and, for optimal control problems, by not having a straightforward way of computing gradients.Deep neural networks have recently been gaining prominence in physics [14]. They provide a robust and versatile toolset for e.g. regression problems, already being used for such diverse applications as boosting the signal-to-noise ratio in LHC collision data [15], establishing a fast mapping between galaxy and dark matter distribution [16], and constructing efficient representations of many-body quantum states [17]. In this work, by introducing a