Search

Strong nonlinear interactions between single photons have important applications in optical quantum information processing. Demonstrations of these interactions in cold atomic ensembles have largely been limited to exploiting slow light generated using electromagnetically induced transparency (EIT). However, these EIT implementations have limited achievable phase shifts due to spontaneous emission. Here, we demonstrate and characterize a scheme free from these limitations using gradient echo memory with inferred single photon phase shifts of $0.07\pm0.02$ $\mu\text{rad}$. Excellent agreement with theoretical modelling was observed. Degradation of memory efficiency was observed for large phase shifts but strategies to overcome that are presented., Comment: 7 pages, 6 figures

Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities., Dynamics in cold atomic ensembles involve complex many-body interactions that are hard to treat analytically. Here, the authors use machine learning to optimise the cooling and trapping of neutral atoms, showing an improvement in the resulting resonant optical depth compared to more traditional solutions.

Quantum repeaters are foundational to establishing the long distance quantum communication channels necessary to create a global communication network [1]. A quantum memory capable of storing optical quantum states is an integral component of the quantum repeater.

The efficiency of an ensemble-based optical quantum memory depends critically on the strength of the atom-light coupling. An optical cavity is an effective method to enhance atom-light coupling strength, with the drawback that cavities can be difficult to integrate into a memory setup. In this work we show coherent enhancement of atom-light coupling via an interference effect. The light to be absorbed into the atomic ensemble is split and used to drive the atoms from opposite ends of the ensemble. We compare this method theoretically to a cavity-enhanced scheme and present experimental results for our coherent enhancement in cold $^{87}\mathrm{Rb}$ atoms that show an efficiency of $72\ifmmode\pm\else\textpm\fi{}5%$ and a storage lifetime of $110\ifmmode\pm\else\textpm\fi{}10\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}\text{s}$.

A number of techniques exist to use an ensemble of atoms as a quantum memory for light. Many of these propose to use backward retrieval as a way to improve the storage and recall efficiency. We report on a demonstration of an off-resonant Raman memory that uses backward retrieval to achieve an efficiency of $65\pm6\%$ at a storage time of one pulse duration. The memory has a characteristic decay time of 60 $\mu$s, corresponding to a delay-bandwidth product of $160$.

Gradient echo memory is the most efficient quantum memory protocol to date. Recent additions of machine learning and compatible single photons can raise its performance and the possibility of using it as a quantum gate.

Quantum information systems demand methods for the storage and manipulation of qubits. For optical qubits, atomic ensembles provide a potential platform for such operations. In this work, we demonstrate a stopped light optical quantum memory with efficiency up to 87%. We also demonstrate and visualise stationary light, which could potentially enhance weak optical nonlinearities. At the heart of our experiments is a laser-cooled atomic ensemble, which has recently been optimised with the help of a machine learning system that uses an artificial neural network.

The X-ray emission spectra due to single-electron capture in 4.54 keV/u Ne10+ collisions with He, Ne and Ar atoms are calculated using the two-center basis generator method. Present results of relative cross sections and X-ray spectra are compared across previous calculations and experimental measurements.