Stopped and stationary light with cold atomic ensembles and machine learning.
B. C. Buchler, Jesse L. Everett, Young‐Wook Cho, Aaron D. Tranter, Harry J. Slatyer, Michael Hush, Karun Paul, Pierre Vernaz-Gris, Anthony C. Leung, Daniel B. Higginbottom, Ping Koy Lam, Geoff Campbell
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.
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.
This cotutelle PhD thesis revolves around quantum optics experiments which involve large atomic ensembles. The study of light-matter interaction and its enhancement are crucial steps in the development and progress of quantum information generation, storage and processing protocols. The work presented here focuses on the evolution of large atomic ensemble preparation techniques, on the development and experimental investigation of stopped and stationary light protocols. Laser-cooled atomic ensembles in both experimental realisations have been brought to optical depths of a few hundreds, at temperatures of tens of microkelvin. Moreover, addressing these ensembles in symmetric configurations has enabled the study of protocols based on the temporal reversal of the mapping of light to collective atomic excitations. These enhancements have led to the storage of qubits based on electromagnetically-induced transparency, and the optical storage in a backward-retrieval Raman scheme, both demonstrating efficiency records, above 50%. This work has also led to the experimental investigation of stationary light and new protocols based on it.
Machine learning now plays a pivotal role in our society, providing solutions to problems that were previously thought intractable. The meteoric rise of this technology can no doubt be attributed to the information age that we now live in. As data is continually amassed, more efficient and scalable methods are required to yield functional models and accurate inferences. Simultaneously we have also seen quantum technology come to the forefront of research and next generation systems. These technologies promise secure information transfer, efficient computation and high precision sensing, at levels unattainable by their classical counterparts. Although these technologies are powerful, they are necessarily more complicated and difficult to control. The combination of these two advances yields an opportunity for study, namely leveraging the power of machine learning to control and optimise quantum (and more generally complex) systems. The work presented in thesis explores these avenues of investigation and demonstrates the potential success of machine learning methods in the domain of quantum and complex systems. One of the most crucial potential quantum technologies is the quantum memory. If we are to one day harness the true power of quantum key distribution for secure transimission of information, and more general quantum computating tasks, it will almost certainly involve the use of quantum memorys. We start by presenting the operation of the cold atom workhorse: the magneto-optical trap (MOT). To use a cold atomic ensemble as a quantum memory we are required to prepare the atoms using a specialised cooling sequence. During this we observe a stable, coherent optical emission exiting each end of the elongated ensemble. We characterise this behaviour and compare it to similar observations in previous work. Following this, we use the ensemble to implement a backward Raman memory. Using this scheme we are able to demonstrate an increased efficiency over that of previous forward recall implementations. While we are limited by the optical depth of the system, we observe an efficiency more than double that of previous implementations. The MOT provides an easily accessible test bed for the optimisation via some machine learning technique. As we require an efficient search method, we implement a new type of algorithm based on deep learning. We design this technique such that the artificial neural networks are placed in control of the online optimisation, rather than simply being used as surrogate models. We experimentally optimise the optical depth of the MOT using this method, by parametrising the time varying compression sequence. We identify a new and unintuitive method for cooling the atomic ensemble which surpasses current methods. Following this initial implementation we make substantial improvements to the deep learning approach. This extends the approach to be applicable to a far wider range of complex problems, which may contain extensive local minima and structure. We benchmark this algorithm against many of the conventional optimisation techniques and demonstrate superior capability to optimise problems with high dimensionality. Finally we apply this technique to a series of preliminary problems, namely the tuning of a single electron transistor and second-order correlations from a quantum dot source.
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.
Quantum communication aims at generating and distributing qubits between distant sites, in order to implement quantum information protocols, such as quantum key distribution, quantum teleportation and quantum processing. For many quantum protocols, and especially for quantum repeaters, which make quantum communication over long distances much more efficient, storage of photonic states is necessary and represents a crucial step towards the realisation of a quantum network. An outstanding challenge of quantum communication is to implement quantum memories for photonic qubits, combining long storage time with high fidelity between the input and output states, and good efficiency. Moreover, to be compatible with quantum network applications, such as quantum repeaters, these memories have to enable heralded storage and on-demand read-out. Cold atomic ensembles hold promise for satisfying both these prerequisites, and are therefore a good candidate as storage medium, offering a compromise between different benchmarks. We are setting up an experiment intended to map polarisation qubits onto an optically pumped cold atomic ensemble of rubidium serving as our quantum memory device.
Quantum communication aims at generating and distributing qubits between distant sites, in order to implement quantum information protocols, such as quantum key distribution, quantum teleportation and quantum processing. For many quantum protocols, and especially for quantum repeaters, which make quantum communication over long distances much more efficient, storage and/or emission of photonic states is necessary and represents a crucial step towards the realisation of a quantum network. An outstanding challenge of quantum communication is to implement quantum memories for photonic qubits, combining long storage time with high fidelity between the input and output states, and good efficiency. Moreover, to be compatible with quantum network applications, such as quantum repeaters, these memories have to enable heralded storage and on-demand read-out. Cold atomic ensembles hold promise for satisfying both these prerequisites, and are therefore a good candidate as storage medium, offering a compromise between different benchmarks. We are setting up an experiment intended to map polarisation qubits onto an optically pumped cold atomic ensemble of rubidium serving as our quantum memory device. In a first development, our quantum memory device will be operated in the single photon emission regime so as to implement, together with an entangled photon-pair source based on guided-wave optics, the quantum teleportation scheme.