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We use an online machine learning algorithm to optimize cooling and loading of rubidium-87 atoms into an evanescent dipole trap array along an optical nanofiber, increasing the number of trapped atoms by 50%.

We use a machine learning optimizer to increase the number of rubidium-87 atoms trapped in an optical nanofiber-based two-color evanescent dipole trap array. Collisional blockade limits the average number of atoms per trap to about 0.5, and a typical uncompensated rubidium trap has even lower occupancy due to challenges in simultaneously cooling atoms and loading them in the traps. Here, we report on the implementation of an in-loop stochastic artificial neural network machine learner to optimize this loading by optimizing the absorption of a near-resonant, nanofiber-guided, probe beam. By giving the neural network control of the laser cooling process, we observe an increase in peak optical depth of 66% from 3.2 ± 0.2 to 5.3 ± 0.3. We use a microscopic model of the atomic absorption to infer an increase in the number of dipole-trapped atoms from 300 ± 60 to 450 ± 90 and a small decrease in their average temperature from 150 to 140 μK. The machine learner is able to quickly and effectively explore the large parameter space of the laser cooling control process so as to find optimal parameters for loading the dipole traps. The increased number of atoms should facilitate studies of collective atom–light interactions mediated via the evanescent field.

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.