Fast machine-learning online optimization of ultra-cold-atom experiments

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system

Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists

We introduce a novel remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians employed a Remote version of their dCRAB optimization algorithm (RedCRAB), and second a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global search. This form of adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This gives encouragement that a much wider range of challenges could potentially be open to gamification in the future

Fast machine-learning online optimization of ultra-cold-atom experiments

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.P. B. Wigley, P. J. Everitt, A. van den Hengel, J. W. Bastian, M. A. Sooriyabandara, G. D. McDonald, K. S. Hardman, C. D. Quinlivan, P. Manju, C. C. N. Kuhn, I. R. Petersen, A. N. Luiten, J. J. Hope, N. P. Robins, M. R. Hus

Quantum metrology with Einstein-Podolsky-Rosen entangled atoms

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Experimental setup for fast BEC generation and number-stabilized atomic ensembles

Ultracold atomic ensembles represent a cornerstone of today’s modern quantum experiments. In particular, the generation of Bose-Einstein condensates (BECs) has paved the way for a myriad of fundamental research topics as well as novel experimental concepts and related applications. As coherent matter waves, BECs promise to be a valuable resource for atom interferometry that allows for high-precision sensing of gravitational fields or inertial moments as accelerations and rotations. In general, the sensitivity of state-of-the-art atom interferometers is fundamentally restricted by the Standard Quantum Limit (SQL). Multi-particle entangled states (e.g. spin-squeezed states, Twin-Fock states, Schrödinger cat states) generated in BECs can be employed to surpass the SQL and shift the sensitivity limit further towards the more fundamental Heisenberg Limit (HL). However, in current real-world atom interferometric applications, ultracold but uncondensed atomic clouds are employed, due to their speed advantage in the sample preparation. The creation of a BEC can take up several tens of seconds, while standard high-precision atom interferometers operate with a cycle rate of several Hz. In addition, the pursued entangled states can be only beneficial if technical noise sources, such as magnetic field or detection noise are not dominating the measurement resolution. These challenges need to be overcome in order to fully exploit the potential sensitivity gain offered by a quantum-enhanced atom interferometer. This thesis describes the design and implementation of a new experimental setup for Heisenberg-limited atom interferometry, which incorporates a high-flux BEC source and the manipulation and detection of atoms at the single-particle level. The presented fast BEC preparation includes a high-flux atom source in a double magneto-optical trap (MOT) configuration that allows to collect 87Rb atoms in a 3D-MOT, which is supplied by a 2D+-MOT with 2×10^10 atoms/s. Forced evaporative cooling of the atoms is divided into two stages, which is sequentially carried out in a magnetic quadrupole trap (QPT) and a crossed-beam optical dipole trap (cODT). The high-flux atom source together with the hybrid evaporation scheme allows to consistently produce BECs with an average of 2×10^5 atoms within 3.5 s. The capabilities of the single-particle resolving detection are demonstrated by realizing a feedback control loop to stabilize the captured number of atoms in a small MOT. A proof-of-principle measurement is demonstrated for the successful stabilization of a target number of 7 atoms with sub-Poissonian fluctuations. The number noise is suppressed by 18 dB below shot noise, which corresponds to a preparation fidelity of 92%. Based on this success, the thesis presents an even improved single-particle resolution. The system comprises a six-channel fiber-based optical setup, which provides independent intensity stabilization and frequency detuning, improved pointing stability as well as a better spatial overlap of the MOT beams. The presented high-speed BEC production combined with accurate atom number preparation and detection, as the two main features of the experimental apparatus, pave the way for a future entanglement-enhanced performance of atom interferometers

Matter-wave optics with Bose-Einstein condensates in microgravity

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Probing Bloch band geometry with ultracold atoms in optical lattices

Ultracold atoms in optical lattices have recently emerged as promising candidates for investigating the geometric and topological aspects of band structures. In this thesis, we exploit the high degree of control available in these systems to directly probe the band geometry of an optical honeycomb lattice. In the first series of experiments, we realize an atomic interferometer in quasimomentum space to measure the singular Berry flux associated with a Dirac point. This technique enables us to determine the distribution of Berry curvature in the Brillouin zone with high quasimomentum resolution. Next, we realize strong-force dynamics that are described by matrix-valued Wilson lines, which are generalizations of the Berry phase to degenerate systems. In this strong-force regime, we show that the evolution in the band populations directly reveals the band geometry. This method enables the reconstruction of both the cell-periodic Bloch states at every quasimomentum and the eigenvalues of Wilson-Zak loops. Our techniques can be used to determine the topological invariants, such as the Chern and Z2 numbers, that characterize the band structure. Lastly, having established our ability to detect the band geometry, we present preliminary experiments on engineering band structures

Machine learning for quantum and complex systems

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