A learning algorithm with emergent scaling behavior for classifying phase transitions

Machine learning-inspired techniques have emerged as a new paradigm for analysis of phase transitions in quantum matter. In this work, we introduce a supervised learning algorithm for studying critical phenomena from measurement data, which is based on iteratively training convolutional networks of increasing complexity, and test it on the transverse field Ising chain and q = 6 Potts model. At the continuous Ising transition, we identify scaling behavior in the classification accuracy, from which we infer a characteristic classification length scale. It displays a power-law divergence at the critical point, with a scaling exponent that matches with the diverging correlation length. Our algorithm correctly identifies the thermodynamic phase of the system and extracts scaling behavior from projective measurements, independently of the basis in which the measurements are performed. Furthermore, we show the classification length scale is absent for the q=6 Potts model, which has a first order transition and thus lacks a divergent correlation length. The main intuition underlying our finding is that, for measurement patches of sizes smaller than the correlation length, the system appears to be at the critical point, and therefore the algorithm cannot identify the phase from which the data was drawn

Deep learning of spatial densities in inhomogeneous correlated quantum systems

Machine learning has made important headway in helping to improve the treatment of quantum many-body systems. A domain of particular relevance are correlated inhomogeneous systems. What has been missing so far is a general, scalable deep-learning approach that would enable the rapid prediction of spatial densities for strongly correlated systems in arbitrary potentials. In this work, we present a straightforward scheme, where we learn to predict densities using convolutional neural networks trained on random potentials. While we demonstrate this approach in 1D and 2D lattice models using data from numerical techniques like Quantum Monte Carlo, it is directly applicable as well to training data obtained from experimental quantum simulators. We train networks that can predict the densities of multiple observables simultaneously and that can predict for a whole class of many-body lattice models, for arbitrary system sizes. We show that our approach can handle well the interplay of interference and interactions and the behaviour of models with phase transitions in inhomogeneous situations, and we also illustrate the ability to solve inverse problems, finding a potential for a desired density

Particle statistics and lossy dynamics of ultracold atoms in optical lattices

Experimental control over ultracold quantum gases has made it possible to investigate low-dimensional systems of both bosonic and fermionic atoms. In closed one-dimensional systems there are many similarities in the dynamics of local quantities for spinless fermions and strongly interacting "hard-core" bosons, which on a lattice can be formalized via a Jordan-Wigner transformation. In this study, we analyze the similarities and differences for spinless fermions and hard-core bosons on a lattice in the presence of particle loss. The removal of a single fermion causes differences in local quantities compared with the bosonic case because of the different particle exchange symmetry in the two cases. We identify deterministic and probabilistic signatures of these dynamics in terms of local particle density, which could be measured in ongoing experiments with quantum gas microscopes

Metal--topological-insulator transition in the quantum kicked rotator with Z2 symmetry

The quantum kicked rotator is a periodically driven dynamical system with a metal-insulator transition. We extend the model so that it includes phase transitions between a metal and a topological insulator, in the universality class of the quantum spin Hall effect. We calculate the Z2 topological invariant using a scattering formulation that remains valid in the presence of disorder. The scaling laws at the phase transition can be studied efficiently by replacing one of the two spatial dimensions with a second incommensurate driving frequency. We find that the critical exponent does not depend on the topological invariant, in agreement with earlier independent results from the network model of the quantum spin Hall effect.Comment: 5 figures, 6 page

NetKet: A machine learning toolkit for many-body quantum systems

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning

Deflection control for reinforced recycled aggregate concrete beams: Experimental database and extension of the fib Model Code 2010 model

Recycled aggregate concrete (RAC) has emerged as a viable solution for solving some of the environmental problems of concrete production. However, design guidelines for deflection control of reinforced RAC members have not yet been proposed. This study presents a comprehensive analysis of the applicability of the fib Model Code 2010 (MC2010) deflection control model to reinforced RAC beams. Three databases of long-term studies on natural aggregate concrete (NAC) and RAC beams were compiled and meta-analyses of deflection predictions by MC2010 were performed. First, the MC2010 deflection control model was tested against a large database of long-term tests on NAC beams. Second, a database of RAC and companion NAC beams was compiled and initial and long-term deflections were calculated using the MC2010 model. It was shown that deflections of RAC beams are significantly underestimated relative to NAC beams. Previously proposed modifications for MC2010 equations for shrinkage strain and creep coefficient were used, and new modifications for the modulus of elasticity and empirical coefficient β were proposed. The improved MC2010 deflection control model on RAC beams was shown to have equal performance to that on companion NAC beams. The proposals presented in this paper can help engineers to more reliably perform deflection control of reinforced RAC members.This is the peer-reviewed version of the article: N. Tošić, S. Marinković, and J. de Brito, ‘Deflection control for reinforced recycled aggregate concrete beams: Experimental database and extension of the fib Model Code 2010 model’, Structural Concrete, vol. 20, no. 6, pp. 2015–2029, 2019 [https://doi.org/10.1002/suco.201900035

NetKet: A machine learning toolkit for many-body quantum systems

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics