Heralded quasi-deterministic entanglement sources based on spontaneous parametric down-conversion

• URL: http://arxiv.org/abs/2603.19489v1
• Date: Thu, 19 Mar 2026 21:39:17 GMT
• Title: Heralded quasi-deterministic entanglement sources based on spontaneous parametric down-conversion
• Abstract summary: A double-heralding technique is presented for producing entangled photon pairs from spontaneous parametric down-conversion (SPDC)<n>This technique is found to yield the most resource-efficient implementation in terms of minimizing the total number of sources and detectors required to achieve a specified rate and fidelity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A double-heralding technique is presented for producing heralded entangled photon pairs from spontaneous parametric down-conversion (SPDC). Compared to the swap-heralded schemes studied in previous cascaded SPDC and zero-added-loss multiplexing (ZALM) proposals, this double-heralding technique is found to yield the most resource-efficient implementation in terms of minimizing the total number of sources and detectors required to achieve a specified rate and fidelity. This is achieved by reducing the number of modes and mode-sorting optics needed on the heralding path. Specifically, by immediately detecting any two signal photons from an array of down-converters, the corresponding idler photons can be projected onto an anti-correlated pair state which is shown to be unitarily equivalent to the state produced by swap-heralded sources, and hence can be used directly for long-range entanglement distribution in a ZALM architecture. Quasi-deterministic operation through two distinct multiplexing techniques is analyzed. The analysis derives expressions for the heralded pair probability and fidelity assuming realistic detectors with losses, dark counts, and partial photon number resolution (PNR), providing a framework for implementation of the source on a photonic integrated circuit (PIC).

Related papers

• Compressed BC-LISTA via Low-Rank Convolutional Decomposition [47.15001096567547]
  We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed forward and backward operators.<n>We propose a Compressed Block-Convolutional (CBC) measurement model based on a low-rank Convolutional Network (CNN) decomposition.
  arXiv  Detail & Related papers  (2026-01-30T16:33:51Z)
• Parallel Diffusion Solver via Residual Dirichlet Policy Optimization [88.7827307535107]
  Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature.<n>Existing solver-based acceleration methods often face significant image quality degradation under a low-dimensional budget.<n>We propose the Ensemble Parallel Direction solver (dubbed as EPD-EPr), a novel ODE solver that mitigates these errors by incorporating multiple gradient parallel evaluations in each step.
  arXiv  Detail & Related papers  (2025-12-28T05:48:55Z)
• Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation [58.19676004192321]
  Diffusion models (DMs), which enable both image generation from noise and inversion from data, have inspired powerful unpaired image-to-image (I2I) translation algorithms. We tackle this problem with Schrodinger Bridges (SBs), which are differential equations (SDEs) between distributions with minimal transport cost. Inspired by this observation, we propose Latent Schrodinger Bridges (LSBs) that approximate the SB ODE via pre-trained Stable Diffusion. We demonstrate that our algorithm successfully conduct competitive I2I translation in unsupervised setting with only a fraction of cost required by previous DM-
  arXiv  Detail & Related papers  (2024-11-22T11:24:14Z)
• 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution [83.09117439860607]
  Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment. It is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. We present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization.
  arXiv  Detail & Related papers  (2024-06-10T06:06:11Z)
• Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays [8.23362366539128]
  Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors.<n>We formulate multiphoton coincidence resolution as an inverse imaging problem and introduce a solution framework to probabilistically resolve the spatial locations of photon incidences.
  arXiv  Detail & Related papers  (2023-12-05T18:58:43Z)
• Imperfect photon detection in quantum illumination [0.0]
  In quantum illumination, various detection schemes have been proposed for harnessing remaining quantum correlations of the entanglement-based resource state. We investigate the performance of this scheme for realistic detection parameters in terms of detection efficiency, dark count probability, and photon number resolution. We find that the requirements for photon number resolution in the two mixer outputs are highly asymmetric due to different associated photon number expectation values.
  arXiv  Detail & Related papers  (2023-08-04T14:18:06Z)
• Quantum super-resolution for imaging two pointlike entangled photon sources [9.590696922408775]
  We investigate the resolution for imaging two pointlike entangled sources by using the method of the moments and the spatial-mode demultiplexing (SPADE) We demonstrate that the separation estimation sensitivity is mainly determined by the photon distribution in each detected modes. In the limiting case of infinitely small source separation, the usage of entangled sources can have better resolution than those using incoherent and coherent sources.
  arXiv  Detail & Related papers  (2023-06-17T02:39:47Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• Optimization of multiplexed single-photon sources operated with photon-number-resolving detectors [0.0]
  Detectors capable of resolving photon numbers have undergone a significant development recently, and this is expected to affect multiplexed periodic single-photon sources. We analyze various spatially and time-multiplexed periodic single-photon source arrangements with photon-number-resolving detectors. We find that the highest single-photon probability is 0.907 that can be achieved by binary bulk time multiplexers using photon-number-resolving detectors.
  arXiv  Detail & Related papers  (2020-03-25T08:46:19Z)
• Optimizing spontaneous parametric down-conversion sources for boson sampling [0.0]
  We show that an optimal trade-off between indistinguishability and losses can always be found for spontaneous downconversion. A 50-photon scattershot boson-sampling experiment using SPDC sources is possible from a computational complexity point of view.
  arXiv  Detail & Related papers  (2020-01-10T18:24:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.