Realization of quantum optical circuits is at the heart of quantum photonic information processing. A long-standing obstacle, however, has been the absence of a suitable platform of single photon sources (SPSs). Such SPSs need to be in spatially ordered arrays and produce, on-demand, highly pure, and indistinguishable single photons with sufficiently uniform emission characteristics to enable controlled interference between photons from distinct sources underpinning functional quantum optical networks. We report on such a platform of SPSs based on a unique class of epitaxial quantum dots dubbed mesa-top single quantum dot. Under resonant excitation, the spatially ordered SPSs (without Purcell enhancement) show single photon purity of >99% [ g ⁽²⁾ (0) ~ 0.015], high two-photon Hong-Ou-Mandel interference visibilities of 0.82 ± 0.03 (at 11.5 kelvin, without cavity), and spectral nonuniformity of <3 nanometers, within established locally tunable technology. Our platform of SPSs paves the path to creating on-chip scalable quantum photonic networks for communication, computation, simulation, sensing and imaging.

Nanophotonic quantum information processing systems require spatially ordered, spectrally uniform single photon sources (SPSs) integrated on-chip with co-designed light manipulating elements providing emission rate enhancement, emitted photon guidance, and lossless propagation. Towards this goal, we consider systems comprising an SPS array with each SPS coupled to a dielectric building block (DBB) based multifunctional light manipulation unit (LMU). For the SPS array, we report triggered single photon emission from GaAs(001)/InGaAs single quantum dots grown selectively on top of nanomesas using the approach of substrate-encoded size-reducing epitaxy (SESRE). Systematic temperature and power dependent photoluminescence (PL), PL excitation, time-resolved PL, and emission statistics studies reveal high spectral uniformity and single photon emission at 8 K with g⁽²⁾(0) of 0.19 ± 0.03. The SESRE based SPS arrays, following growth of a planarizing overlayer, are readily integrable with LMUs fabricated subsequently using either the 2D photonic crystal approach or, as theoretically examined here, DBB based LMUs. We report the simulated optical response of SPS embedded in DBB based nanoantenna-waveguide structures as the multifunctional LMU. The multiple functions of emission rate enhancement, guiding, and lossless propagation are derived from the behavior of the same collective Mie resonance (dominantly magnetic) of the interacting DBB based LMU tuned to the SPS targeted emission wavelength of 980 nm. The simulation utilizes an analytical approach that provides physical insight into the obtained numerical results. Together, the combined experimental and modelling demonstrations open a rich approach to implementing co-designed on-chip integrated SPS-LMUs that, in turn, serve as basic elements of integrated nanophotonic information processing systems.

A long standing obstacle to realizing highly sought on-chip monolithic solid state quantum optical circuits has been the lack of a starting platform comprising scalable spatially ordered and spectrally uniform on-demand single photon sources (SPSs) buried under a planar surface. In this paper, we report on the first realization of planarized SPS arrays based on a unique class of shape-controlled single quantum dots (SQDs) synthesized on mesa top (dubbed MTSQDs) using substrate-encoded size-reducing epitaxy (SESRE) on spatially regular arrays of patterned nanomesas with edge orientation chosen to drive symmetric adatom migration from the nanomesa sidewalls to the top, thereby enabling spatially selective growth. Specifically, on GaAs(001) square nanomesas with edges along $⟨100⟩$ , we synthesized binary GaAs/InAs/GaAs MTSQDs emitting around 1120 nm with 1.8 nm standard deviation and single photon emission purity >99.5%. SESRE based MTSQDs are shown for the first time to lend themselves to planarization of the surface morphology when grown on pedestal shape mesas. We demonstrate that the planarizing overgrowth process over arrays of InGaAs SQDs largely maintains the SQDs’ high single photon emission purity (>98%) and spectral uniformity (∼5 nm). Such planarized SQD arrays offer the long-sought platform for on-chip integration with light manipulating structures to realize quantum optical circuits.

Realizing ordered and spectrally uniform single photon source arrays integrable on-chip with light manipulating elements in a scalable architecture lies at the core of building monolithic quantum optical circuits (QOCs). We demonstrate here a spatially ordered 5 × 8 array of surface-curvature driven mesa-top GaAs(001)/InGaAs/GaAs single quantum dots (MTSQDs) that exhibit highly pure (∼99%) single photon emission as deduced from the measured g⁽²⁾(0) < 0.02 at 9.4 K. Polarization-independent and polarization-resolved high resolution photoluminescence measurements show that these ordered and spectrally uniform QDs have neutral exciton emission with an intrinsic linewidth ∼ 10 μeV and a fine structure splitting <10 μeV, an important figure of merit for the use of QDs in QOCs. The findings point to the high potential of using such MTSQD based single photon source arrays as a promising platform for on-chip scalable integration with light manipulating units (connected resonant cavity, waveguide, beam splitter, etc.) to enable constructing QOCs.

Developing resource allocation algorithms with strong real-time and high efficiency has been an imperative topic in wireless networks. Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems. This paper studies learning-based efficient massive beamforming methods for multi-user MIMO networks. The considered massive beamforming problem is challenging in two aspects. First, the beamforming matrix to be learned is quite high-dimensional in case with a massive number of antennas. Second, the objective is often time-varying and the solution space is not fixed due to some communication requirements. All these challenges make learning representation for massive beamforming an extremely difficult task. In this paper, by exploiting the structure of the most popular WMMSE beamforming solution, we propose convolutional massive beamforming neural networks (CMBNN) using both supervised and unsupervised learning schemes with particular design of network structure and input/output. Numerical results demonstrate the efficacy of the proposed CMBNN in terms of running time and system throuuhput,

Location-based services (LBS) have gained huge popularity because of the easy availability of modern mobile devices and the fast development of geographical information science (GIS). However, the lack of protection for private user positions might give rise to privacy concerns. This kind of problem is especially serious in mobile application environment because many mobile applications tend to use LBS. In this paper, we propose a new privacy preserving approach using customized robust cloaked region (RCR), depending on a peer-to-peer structure and the premise that users do not trust each other when sharing their geographical locations. Two algorithms are used to generate the RCR with high user density. The area of the RCR is controlled by the user’s demanded degree of protection. To enhance the resistance to regional background knowledge attack, we incorporate a location semantic value into each unit of the user map. According to extensive simulations, our method can effectively obfuscate a user’s geographical location into a highly indistinguishable region because of the disturbance of nearby users and different equally possible locations.