Showing papers by "Daniel J. Gauthier published in 2020"

Drone-Based Quantum Key Distribution

Abstract: Current quantum cryptography implementations focus on fiber-based or fixed free-space point-to-point channels. We seek to expand this to quantum communication from mobile platforms. Here, we report progress towards tracking system stabilization and air-to-air signal coupling.

Multimode Time-Delay Interferometer for Free-Space Quantum Communication

Abstract: Optical communication via free-space channels is attractive for establishing long-range secure quantum networks, but is often hindered by deleterious effects on the transverse spatial mode profile of the beam, caused by propagation through the atmosphere. Additionally, interferometric measurement at the receiver becomes much more difficult with multimode profiles. This study presents a compact time-delay interferometer with high stability and interference visibility for single- and multimode spatial profiles. The results of this study are important for the future of quantum communication networks as well as a wide range of techniques for classical and quantum optical measurement.

Submillisecond, nondestructive, time-resolved quantum-state readout of a single, trapped neutral atom

Abstract: We achieve fast, nondestructive quantum-state readout via fluorescence detection of a single $^{87}\mathrm{Rb}$ atom in the $5{S}_{1/2}$ $(F=2)$ ground state held in an optical dipole trap. The atom is driven by linearly polarized readout laser beams, making the scheme insensitive to the distribution of atomic population in magnetic sublevels. We demonstrate a readout fidelity of $97.6\ifmmode\pm\else\textpm\fi{}0.2%$ in a readout time of $160\ifmmode\pm\else\textpm\fi{}20\phantom{\rule{4pt}{0ex}}\ensuremath{\mu}\mathrm{s}$ with the atom retained in $g97%$ of the trials, representing an advancement over other magnetic-state-insensitive techniques. We demonstrate that the $F=2$ state is partially protected from optical pumping by the distribution of the dipole matrix elements for the various transitions and the ac-Stark shifts from the optical trap. Our results are likely to find application in neutral-atom quantum computing and simulation.

Model-Free Control of Dynamical Systems with Deep Reservoir Computing

Abstract: We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories.