A scanning probe-based pick-and-place procedure for assembly of integrated quantum optical hybrid devices

Integrated quantum optical hybrid devices consist of fundamental constituents such as single emitters and tailored photonic nanostructures. A reliable fabrication method requires the controlled deposition of active nanoparticles on arbitrary nanostructures with highest precision. Here, we describe an easily adaptable technique that employs picking and placing of nanoparticles with an atomic force microscope combined with a confocal setup. In this way, both the topography and the optical response can be monitored simultaneously before and after the assembly. The technique can be applied to arbitrary particles. Here, we focus on nanodiamonds containing single nitrogen vacancy centers, which are particularly interesting for quantum optical experiments on the single photon and single emitter level.Comment: The following article has been submitted to Review of Scientific Instruments. After it is published, it will be found at http://rsi.aip.org

Simple atomic quantum memory suitable for semiconductor quantum dot single photons

Quantum memories matched to single photon sources will form an important cornerstone of future quantum network technology. We demonstrate such a memory in warm Rb vapor with on-demand storage and retrieval, based on electromagnetically induced transparency. With an acceptance bandwidth of $\delta f$ = 0.66~GHz the memory is suitable for single photons emitted by semiconductor quantum dots. In this regime, vapor cell memories offer an excellent compromise between storage efficiency, storage time, noise level, and experimental complexity, and atomic collisions have negligible influence on the optical coherences. Operation of the memory is demonstrated using attenuated laser pulses on the single photon level. For 50 ns storage time we measure $\eta_{\textrm{e2e}}^{\textrm{50ns}} = 3.4(3)\%$ \emph{end-to-end efficiency} of the fiber-coupled memory, with an \emph{total intrinsic efficiency} $\eta_{\textrm{int}} = 17(3)\%$. Straightforward technological improvements can boost the end-to-end-efficiency to $\eta_{\textrm{e2e}} \approx 35\%$; beyond that increasing the optical depth and exploiting the Zeeman substructure of the atoms will allow such a memory to approach near unity efficiency. In the present memory, the unconditional readout noise level of $9\cdot 10^{-3}$ photons is dominated by atomic fluorescence, and for input pulses containing on average $\mu_{1}=0.27(4)$ photons the signal to noise level would be unity

Single-Photon Storage in a Ground-State Vapor Cell Quantum Memory

Interfaced single-photon sources and quantum memories for photons together form a foundational component of quantum technology. Achieving compatibility between heterogeneous, state-of-the-art devices is a long-standing challenge. We built and successfully interfaced a heralded single-photon source based on cavity-enhanced spontaneous parametric down-conversion in ppKTP and a matched memory based on electromagnetically induced transparency in warm $^{87}$Rb vapor. The bandwidth of the photons emitted by the source is 370 MHz, placing its speed in the technologically relevant regime while remaining well within the acceptance bandwidth of the memory. Simultaneously, the experimental complexity is kept low, with all components operating at or above room temperature. Read-out noise of the memory is considerably reduced by exploiting polarization selection rules in the hyperfine structure of spin-polarized atoms. For the first time, we demonstrate single-photon storage and retrieval in a ground-state vapor cell memory, with $g_{c,\text{ret}}^{(2)}=0.177(23)$ demonstrating the single-photon character of the retrieved light. Our platform of single-photon source and atomic memory is attractive for future experiments on room-temperature quantum networks operating at high bandwidth.Comment: 9 pages, 5 figure

Enhancement of the Zero Phonon Line emission from a Single NV-Center in a Nanodiamond via Coupling to a Photonic Crystal Cavity

Using a nanomanipulation technique a nanodiamond with a single nitrogen vacancy center is placed directly on the surface of a gallium phosphide photonic crystal cavity. A Purcell-enhancement of the fluorescence emission at the zero phonon line (ZPL) by a factor of 12.1 is observed. The ZPL coupling is a first crucial step towards future diamond-based integrated quantum optical devices

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible