Frequency Multiplexing for Quasi-Deterministic Heralded Single-Photon Sources

Single-photon sources based on optical parametric processes have been used extensively for quantum information applications due to their flexibility, room-temperature operation and potential for photonic integration. However, the intrinsically probabilistic nature of these sources is a major limitation for realizing large-scale quantum networks. Active feedforward switching of photons from multiple probabilistic sources is a promising approach that can be used to build a deterministic source. However, previous implementations of this approach that utilize spatial and/or temporal multiplexing suffer from rapidly increasing switching losses when scaled to a large number of modes. Here, we break this limitation via frequency multiplexing in which the switching losses remain fixed irrespective of the number of modes. We use the third-order nonlinear process of Bragg scattering four-wave mixing as an efficient ultra-low noise frequency switch and demonstrate multiplexing of three frequency modes. We achieve a record generation rate of $4.6\times10^4$ multiplexed photons per second with an ultra-low $g^{2}(0)$ = 0.07, indicating high single-photon purity. Our scalable, all-fiber multiplexing system has a total loss of just 1.3 dB independent of the number of multiplexed modes, such that the 4.8 dB enhancement from multiplexing three frequency modes markedly overcomes switching loss. Our approach offers a highly promising path to creating a deterministic photon source that can be integrated on a chip-based platform.Comment: 28 pages, 9 figures. Comments welcom

Fast recursive filters for simulating nonlinear dynamic systems

A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster than 4th-order Runge-Kutta. The computational scheme has minimal memory requirements, and is also suited for computation on a stream processor, such as a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta integration shows that the new algorithm is 1-2 orders of magnitude faster. The paper is in press now at Neural Computatio