Using quantum states of light to probe the retinal network

• URL: http://arxiv.org/abs/2111.03285v5
• Date: Wed, 20 Jul 2022 14:10:40 GMT
• Title: Using quantum states of light to probe the retinal network
• Authors: Ali Pedram, \"Ozg\"ur E. M\"ustecapl{\i}o\u{g}lu, Iannis K. Kominis
• Abstract summary: The ability of rod cells to sense a few photons has implications for understanding the capabilities of the human visual and nervous system. We investigate the fundamental metrological capabilities of different quantum states of light to probe the retina.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The minimum number of photons necessary for activating the sense of vision has been a topic of research for over a century. The ability of rod cells to sense a few photons has implications for understanding the fundamental capabilities of the human visual and nervous system and creating new vision technologies based on photonics. We investigate the fundamental metrological capabilities of different quantum states of light to probe the retina, which is modeled using a simple neural network. Stimulating the rod cells by Fock, coherent and thermal states of light, and calculating the Cramer-Rao lower bound (CRLB) and Fisher information matrix for the signal produced by the ganglion cells in various conditions, we determine the volume of minimum error ellipsoid. Comparing the resulting ellipsoid volumes, we determine the metrological performance of different states of light for probing the retinal network. The results indicate that the thermal state yields the largest error ellipsoid volume and hence the worst metrological performance, and the Fock state yields the best performance for all parameters. This advantage persists even if another layer is added to the network or optical losses are considered in the calculations.

Related papers

• Time-bin entanglement in the deterministic generation of linear photonic cluster states [0.0]
  We theoretically investigate strategies for the deterministic creation of trains of time-bin entangled photons using an individual quantum emitter. We show the theoretical generation of linear cluster states with substantial numbers of entangled photonic qubits in full microscopic numerical simulations.
  arXiv  Detail & Related papers  (2024-03-13T13:38:45Z)
• Design and simulation of a transmon qubit chip for Axion detection [103.69390312201169]
  Device based on superconducting qubits has been successfully applied in detecting few-GHz single photons via Quantum Non-Demolition measurement (QND) In this study, we present Qub-IT's status towards the realization of its first superconducting qubit device.
  arXiv  Detail & Related papers  (2023-10-08T17:11:42Z)
• Spatially Varying Nanophotonic Neural Networks [39.1303097259564]
  Photonic processors that execute operations using photons instead of electrons promise to enable optical neural networks with ultra-low latency and power consumption. Existing optical neural networks, limited by the underlying network designs, have achieved image recognition accuracy far below that of state-of-the-art electronic neural networks.
  arXiv  Detail & Related papers  (2023-08-07T08:48:46Z)
• Quantum-noise-limited optical neural networks operating at a few quanta per activation [5.494796517705931]
  We show that it is possible to train optical neural networks to perform deterministic image-classification tasks with high accuracy. We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime.
  arXiv  Detail & Related papers  (2023-07-28T17:59:46Z)
• Database of semiconductor point-defect properties for applications in quantum technologies [54.17256385566032]
  We have calculated over 50,000 point defects in various semiconductors including diamond, silicon carbide, and silicon. We characterize the relevant optical and electronic properties of these defects, including formation energies, spin characteristics, transition dipole moments, zero-phonon lines. We find 2331 composite defects which are stable in intrinsic silicon, which are then filtered to identify many new optically bright telecom spin qubit candidates and single-photon sources.
  arXiv  Detail & Related papers  (2023-03-28T19:51:08Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes [47.187609203210705]
  In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. Here, we present a novel algorithm based on deep convolutional neural networks to reconstruct the site-resolved lattice occupation with high fidelity.
  arXiv  Detail & Related papers  (2022-12-22T18:57:27Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• All-Optical Nuclear Quantum Sensing using Nitrogen-Vacancy Centers in Diamond [52.77024349608834]
  Microwave or radio-frequency driving poses a significant limitation for miniaturization, energy-efficiency and non-invasiveness of quantum sensors. We overcome this limitation by demonstrating a purely optical approach to coherent quantum sensing. Our results pave the way for highly compact quantum sensors to be employed for magnetometry or gyroscopy applications.
  arXiv  Detail & Related papers  (2022-12-14T08:34:11Z)
• Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing [0.0]
  Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. We propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors.
  arXiv  Detail & Related papers  (2021-05-22T09:28:51Z)
• All-optical neural network quantum state tomography [0.39146761527401414]
  We build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN) Experiment results demonstrate the validity and efficiency of the all-optical setup. Our all-optical setup of integrated AONN-QST may shed light on replenishing the all-optical quantum network with the last brick.
  arXiv  Detail & Related papers  (2021-03-11T04:43:34Z)

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.