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  1. Variational Benchmarks for Quantum Many-Body Problems

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  2. Neural-Network Quantum States for Periodic Systems in Continuous Space

    Publication
    We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parametrized in terms …

  3. Gauge equivariant neural networks for quantum lattice gauge theories

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    Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials …

  4. Variational Monte Carlo calculations of A≤4 nuclei with an artificial neural-network correlator ansatz

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    The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neural networks …

  5. Continuous-variable neural-network quantum states and the quantum rotor model

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    We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization. A simple family of continuous-variable trial wavefunctons …

  6. Fermionic Wave Functions from Neural-Network Constrained Hidden States

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    We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert …

  7. Precise measurement of quantum observables with neural-network estimators

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    The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundamental limitation is particularly …

  8. Quantum process tomography with unsupervised learning and tensor networks

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    The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of …

  9. Natural evolution strategies and variational Monte Carlo

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    A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization …

  10. Deep learning the Hohenberg-Kohn maps of Density Functional Theory

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    A striking consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of a bijection between the local density and the ground-state many-body …

  11. Fermionic neural-network states for ab-initio electronic structure

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  12. Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States

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  13. Quantum Natural Gradient

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  14. NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems

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  15. Neural-Network Approach to Dissipative Quantum Many-Body Dynamics

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  16. Restricted Boltzmann machines in quantum physics

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  17. Machine learning and the physical sciences

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  18. Ground state phase diagram of the one-dimensional Bose-Hubbard model from restricted Boltzmann machines

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  19. Deep autoregressive models for the efficient variational simulation of many-body quantum systems

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  20. Quantum Simulators: Architectures and Opportunities

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  21. Symmetries and many-body excited states with neural-network quantum states

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  22. Neural-network states for the classical simulation of quantum computing

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  23. Single-atom-resolved probing of lattice gases in momentum space

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    Measuring the full distribution of individual particles is of fundamental importance to characterize many-body quantum systems through correlation functions at any order. Here, we demonstrate the possibility …

  24. Universal Scaling Laws for Correlation Spreading in Quantum Systems with Short- and Long-Range Interactions

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    The spreading of correlations after a quantum quench is studied in a wide class of lattice systems, with short- and long-range interactions. Using a unifying quasiparticle framework …

  25. Learning hard quantum distributions with variational autoencoders

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    The exact description of many-body quantum systems represents one of the major challenges in modern physics, because it requires an amount of computational resources that scales exponentially …

  26. Constructing exact representations of quantum many-body systems with deep neural networks

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    We develop a constructive approach to generate artificial neural networks representing the exact ground states of a large class of many-body lattice Hamiltonians. It is based on …

  27. Neural-network quantum state tomography

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    The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods to validate and fully exploit quantum resources. Quantum state tomography (QST …