Oscillation and decay of particle current due to a quench and dephasing in an interacting fermionic system

We study the response of a particle current to dissipative dephasing in an interacting, few-body fermionic lattice system. The particles are prepared in the ground state in presence of an artificial magnetic gauge field, which is subsequently quenched to zero. The initial current decays non-trivially in the dissipative environment and we explore the emerging dynamics and its dependence on various system parameters.Comment: 6 pages, 5 figures, submitted to European Physical Journal: Special Topic (EPJ-ST

Measurement of the entanglement spectrum of a symmetry-protected topological state using the IBM quantum computer

Entanglement properties are routinely used to characterize phases of quantum matter in theoretical computations. For example the spectrum of the reduced density matrix, or so-called "entanglement spectrum", has become a widely used diagnostic for universal topological properties of quantum phases. However, while being convenient to calculate theoretically, it is notoriously hard to measure in experiments. Here we use the IBM quantum computer to make the first ever measurement of the entanglement spectrum of a symmetry-protected topological state. We are able to distinguish its entanglement spectrum from those we measure for trivial and long-range ordered states.Comment: 8 pages, 4 figure

Neural Network Evolution Strategy for Solving Quantum Sign Structures

Feed-forward neural networks are a novel class of variational wave functions for correlated many-body quantum systems. Here, we propose a specific neural network ansatz suitable for systems with real-valued wave functions. Its characteristic is to encode the all-important rugged sign structure of a quantum wave function in a convolutional neural network with discrete output. Its training is achieved through an evolutionary algorithm. We test our variational ansatz and training strategy on two spin-1/2 Heisenberg models, one on the two-dimensional square lattice and one on the three-dimensional pyrochlore lattice. In the former, our ansatz converges with high accuracy to the analytically known sign structures of ordered phases. In the latter, where such sign structures are a priory unknown, we obtain better variational energies than with other neural network states. Our results demonstrate the utility of discrete neural networks to solve quantum many-body problems

Poster: Improving communication and communicability with smarter use of text-based messages on mobile and wearable devices

Ministry of Education, Singapore under its Academic Research Funding Tier 2; Singapore National Research Foundation under IDM Futures Funding Initiativ