Single spin probe of Many-Body Localization

We use an external spin as a dynamical probe of many body localization. The probe spin is coupled to an interacting and disordered environment described by a Heisenberg spin chain in a random field. The spin-chain environment can be tuned between a thermalizing delocalized phase and non-thermalizing localized phase, both in its ground- and high-energy states. We study the decoherence of the probe spin when it couples to the environment prepared in three states: the ground state, the infinite temperature state and a high energy N\'eel state. In the non-thermalizing many body localized regime, the coherence shows scaling behaviour in the disorder strength. The long-time dynamics of the probe spin shows a logarithmic dephasing in analogy with the logarithmic growth of entanglement entropy for a bi-partition of a many-body localized system. In summary, we show that decoherence of the probe spin provides clear signatures of many-body localization.Comment: 5 pages, 4 figure

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine (RBM) wavefunctions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis, and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the R\'enyi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware.Comment: 15 pages, 13 figure

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

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator by means of a neural-network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine wave functions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the Rényi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware

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

The Subtle Signaling Strength of Smells: A Masked Odor Enhances Interpersonal Trust.

Most everyday smells, from lavender to body odors, are complex odorant mixtures that "host" particular compounds that guide (social) behavior and motivation (biomarkers). A key element of social behavior is interpersonal trust, and building on previous research showing that (i) lavender odor can enhance trust, and that (ii) certain compounds in body odor can reduce stress in mice and humans (called "social buffering"), we examined whether a grassy-smelling compound found in both body odors and lavender, hexanal, would enhance interpersonal trust. Notably, we applied odor masking to explore whether trust could be influenced subconsciously by masked (i.e., undetectable) hexanal. In Study 1 (between-subjects), 90 females played a Trust Game while they either smelled hexanal (0.01% v/v), clove odor (eugenol: 10% v/v), or hexanal masked by clove odor (a mix of the former). As a sign of higher trust, participants gave more money to a trustee while exposed to masked hexanal (vs. the mask: eugenol). In Study 2 (within-subjects, double-blind), another sample of 35 females smelled the same three odors, while they rated the trustworthiness of a spectrum of faces that varied on trustworthiness. Controlling for subjective odor intensity and pleasantness and substantiating that masked hexanal could not be distinguished from the mask, faces were perceived as more trustworthy during exposure to masked hexanal (vs. the mask: eugenol). Whereas non-masked hexanal also increased face trustworthiness ratings, these effects disappeared after controlling for the odor's subjective intensity and pleasantness. The combined results bring new evidence that trust can be enhanced implicitly via undetected smells