Dynamics of quasiparticles in graphene under intense circularly polarized light

A monolayer of graphene irradiated with circularly polarized light suggests a unique platform for surface electromagnetic wave (plasmon-polariton) manipulation. In fact, the time periodicity of the Hamiltonian leads to a geometric Aharonov-Anandan phase and results in a photovoltaic Hall effect in graphene, creating off-diagonal components of the conductivity tensor. The latter drastically changes the dispersion relation of surface plasmon-polaritons, leading to hybrid wave generation. In this paper we present a systematic and self-contained analysis of the hybrid surface waves obtained from Maxwell equations based on a microscopic formula for the conductivity. We consider a practical example of graphene sandwiched between two dielectric media and show that in the one-photon approximation there is formation of propagating hybrid surface waves. From this analysis emerges the possibility of a reliable experimental realization to study Zitterbewegung of charge carriers of graphene.Comment: 9 pages, 4 figure

Impact of high-frequency pumping on anomalous finite-size effects in three-dimensional topological insulators

Lowering of the thickness of a thin-film three-dimensional topological insulator down to a few nanometers results in the gap opening in the spectrum of topologically protected two-dimensional surface states. This phenomenon, which is referred to as the anomalous finite-size effect, originates from hybridization between the states propagating along the opposite boundaries. In this work, we consider a bismuth-based topological insulator and show how the coupling to an intense high-frequency linearly polarized pumping can further be used to manipulate the value of a gap. We address this effect within recently proposed Brillouin-Wigner perturbation theory that allows us to map a time-dependent problem into a stationary one. Our analysis reveals that both the gap and the components of the group velocity of the surface states can be tuned in a controllable fashion by adjusting the intensity of the driving field within an experimentally accessible range and demonstrate the effect of light-induced band inversion in the spectrum of the surface states for high enough values of the pump.Comment: 6 pages, 3 figure

A spin dynamics approach to solitonics

It is spatial dispersion which is exclusively responsible for the emergence of exchange interaction and magnetic ordering. In contrast, magneto-crystalline anisotropy present in any realistic material brings in a certain non-linearity to the equation of motion. Unlike homogeneous ferromagnetic ordering a variety of non-collinear ground state configurations emerge as a result of competition among exchange, anisotropy, and dipole-dipole interaction. These particle-like states, e.g. magnetic soliton, skyrmion, domain wall, form a spatially localised clot of magnetic energy. In this paper we explore topologically protected magnetic solitons that might potentially be applied for logical operations and/or information storage in the rapidly advancing filed of solitonics (and skyrmionics). An ability to easily create, address, and manipulate such structures is among the prerequisite forming a basis of -onics technology, and is investigated in detail here using numerical and analytical tools

Quantum-enhanced policy iteration on the example of a mountain car

Advances in the experimental demonstration of quantum processors have provoked a surge of interest to the idea of practical implementation of quantum computing over last years. It is expected that the use of quantum algorithms will significantly speed up the solution to certain problems in numerical optimization and machine learning. In this paper, we propose a quantum-enhanced policy iteration (QEPI) algorithm as widely used in the domain of reinforcement learning and validate it with the focus on the mountain car problem. In practice, we elaborate on the soft version of the value iteration algorithm, which is beneficial for policy interpretation, and discuss the stochastic discretization technique in the context of continuous state reinforcement learning problems for the purposes of QEPI. The complexity of the algorithm is analyzed for dense and (typical) sparse cases. Numerical results on the example of a mountain car with the use of a quantum emulator verify the developed procedures and benchmark the QEPI performance.Comment: 12 pages, 7 figure

Tensor train optimization of parametrized quantum circuits

We examine a particular realization of derivative-free method as implemented on tensor train based optimization to the variational quantum eigensolver. As an example, we consider parametrized quantum circuits composed of a low-depth hardware-efficient ansatz and Hamiltonian variational ansatz for addressing the ground state of the transverse field Ising model. We further make a comparison with gradient-based optimization techniques and discuss on the advantage of using tensor train based optimization, especially in the presence of noise.Comment: 7 pages, 5 figure