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The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasi-stochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits with the Transformer, a powerful ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits which build GHZ and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on six qubits. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems., Comment: 12 pages, 11 figures including supplementary materials

A major bottleneck in the quest for scalable many-body quantum technologies is the difficulty in benchmarking their preparations, which suffer from an exponential `curse of dimensionality' inherent to their quantum states. We present an experimentally friendly method for density matrix reconstruction based on deep neural-network generative models. The learning procedure comes with a built-in approximate certificate of the reconstruction and makes no assumptions on the state under scrutiny, making it both reliable and unconditional. It can efficiently handle a broad class of complex systems including prototypical states in quantum information, as well as ground states of local spin models common to condensed matter physics. The key insight is to reduce the state tomography task to an unsupervised learning problem of the statistics of an informationally complete set of quantum measurements. This constitutes a modern machine learning approach to the validation of large quantum devices, which may prove relevant as a neural-network ansatz over mixed states suitable for variational optimization., Comment: 29 pages, 9 figures. Includes appendix. Some typos in the description of the POVMs have been fixed. The numerically generated measurements, the implementation of the generative models, and the code to numerically generate the data sets used in this manuscript are available at https://github.com/carrasqu/POVM_GENMODEL

One of the greatest challenges in quantum information processing is the coherent control over quantum systems with an ever increasing number of particles. Within this endeavor, the harnessing of many-body entanglement against the effects of the environment is a pressing issue. Besides being an important concept from a fundamental standpoint, entanglement is recognized as a crucial resource for performance enhancements over classical methods. Understanding and controlling many-body entanglement in open systems may have implications in quantum computing, quantum simulations, secure quantum communication, quantum metrology, our understanding of the quantum-to-classical transition, and other important questions of quantum foundations. Here we present an overview of recent theoretical and experimental efforts to underpin the dynamics of entanglement in open quantum systems. Entanglement is taken as a dynamic quantity, and we survey how it evolves due to the interaction of the entangled system with its surroundings. We analyze several scenarios, corresponding to different families of states and environments, which render a diversity of dynamical behaviors. Contrary to single-particle quantities, that typically vanish only asymptotically in time, entanglement may disappear at a finite time. Moreover, important classes of entanglement show an exponential decay with the system size when subject to local noise, posing yet another threat to the already challenging scaling of quantum technologies. Results for the local and global noise cases are summarized. Robustness-enhancement techniques, scaling laws, statistical and geometrical aspects of multipartite-entanglement decay are also reviewed; all in order to give a broad picture of entanglement dynamics in open quantum systems addressed to both theorists and experimentalists inside and outside the field of quantum information., Comment: 76 pages, 32 figures. Long-overdue review article. Preliminary version. Comments are very welcome