Related papers: Electromagnetically-induced transparency assists t…
In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to perform backpropagation through linear layers. Since the…
We present experimental observations of interference between an atomic spin coherence and an optical field in a {\Lambda}-type gradient echo memory. The interference is mediated by a strong classical field that couples a weak probe field to…
High-visibility interference of photon echoes generated in spatially separated solid-state atomic ensembles is demonstrated. The solid state ensembles were LiNbO$_3$ waveguides doped with Erbium ions absorbing at 1.53 $\mu$m. Bright…
In continuous-variable quantum information processing, it is crucial to develop high-efficiency and broadband quantum memory of squeezed light, which enables the storage of full-bandwidth information. Here, we present a quantum memory of…
We study optomechanically induced transparency (OMIT) in a compound system consisting of coupled optical resonators and a mechanical mode, focusing on the unconventional role of loss. We find that optical transparency can emerge at the…
Microwave-to-optical quantum transducers will enable coherent interconnection between distant superconducting quantum devices. Ongoing explorations with several platforms have shown promising results at single-photon levels. However, in all…
Microwave-optical transducers and quantum memories are fundamental components of quantum repeaters, essential for developing a quantum internet in which solid-state quantum computers serve as nodes interconnected by optical fibers for data…
High-density and ultracold atomic gases have emerged as promising media for storage of individual photons for quantum memory applications. In this paper we provide an overview of our theoretical and experimental efforts in this direction,…
Quantum memory is one of key ingredients consisting of quantum networks, therefore storing light at single photon level is an important step for realizing quantum networks. A photon, encoded in a high-dimensional state, can significantly…
We study a dephasing channel with memory, modelled by a multimode environment of oscillators. Focusing on the case of two channel uses, we show that memory effects can enhance the amount of coherent quantum information transmitted down the…
Few-shot meta-learning presents a challenge for gradient descent optimization due to the limited number of training samples per task. To address this issue, we propose an episodic memory optimization for meta-learning, we call EMO, which is…
We present a quantum multi-modal treatment describing Electromagnetically Induced Transparency (EIT) as a mechanism for storing continuous variable quantum information in light fields. Taking into account the atomic noise and decoherences…
This work presents a comprehensive theoretical comparison between absorption-based and electromagnetically induced transparency (EIT)-based atomic gradient sensing in a four-level tripod system. Both methods were evaluated under identical…
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for…
Understanding how atoms collectively interact with light is not only important for fundamental science, but also crucial for designing light-matter interfaces in quantum technologies. Over the past decades, numerous studies have focused on…
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based…
Effective multi-mode photon echo based quantum memory on multi-atomic ensemble in the QED cavity is proposed. Analytical solution is obtained for the quantum memory efficiency that can be equal unity when optimal relations for the cavity…
We propose a simple, but efficient and accurate machine learning (ML) model for developing high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical…
Quantum memories feature a reversible conversion of optical fields into long-lived atomic spin waves, and are therefore ideal for operating as sensitive atomic sensors. However, up to now, atom-light interferometers have lacked an efficient…
Equalizer parameter optimization for signal integrity in high-speed Dynamic Random Access Memory systems is crucial but often computationally demanding or model-reliant. This paper introduces a data-driven framework employing learned latent…