Related papers: Direct coupling of nonlinear integrated cavities f…
Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few inter-module connections are sufficient to match the…
Over the past decade, strong interactions of light and matter at the single-photon level have enabled a wide set of scientific advances in quantum optics and quantum information science. This work has been performed principally within the…
We present the analysis of emission profile of downconverted photons from a critically phase-matched nonlinear crystal. This is done via direct imaging of down converted photons by a CMOS camera. The effects of nonlinear crystal thickness…
One of the most studied model systems in quantum optics is a two-level atom strongly coupled to a single mode of the electromagnetic field stored in a cavity, a research field named cavity quantum electrodynamics or CQED. CQED has recently…
While experiments with one or two quantum emitters have become routine in various laboratories, scalable platforms for efficient optical coupling of many quantum systems remain elusive. To address this issue, we report on chip-based systems…
We provide a thorough theoretical analysis of qubit state measurement in a setup where a driven, parametrically-coupled cavity system is directly coupled to the qubit, with one of the cavities having a weak Kerr nonlinearity. Such a system…
Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint…
Deep Reservoir Computing has emerged as a new paradigm for deep learning, which is based around the reservoir computing principle of maintaining random pools of neurons combined with hierarchical deep learning. The reservoir paradigm…
Manifesting across all time, mass and length scales, nonlinearities lie at the core of numerous physical phenomena. Next-generation quantum applications, such as quantum sensing, require the combination of nonlinearity with non-classical…
Photonics has unlocked the potential for energy-efficient acceleration of deep learning. Most approaches toward photonic deep learning have diligently reproduced traditional deep learning architectures using photonic platforms, separately…
We investigate nonlinear effects in an electromechanical system consisting of a superconducting charge qubit coupled to transmission line resonator and a nanomechanical oscillator, which in turn is coupled to another transmission line…
Photonic chip-based frequency combs have emerged as a transformative platform, enabling compact, scalable, and high-performance multiwavelength sources with far-reaching impact across science and technology. Most commonly, these sources…
Rapid development in integrated optoelectronic devices and quantum photonic architectures creates a need for optical fiber to chip coupling with low losses. Here we present a fast and generic approach that allows temperature stable…
Ultracompact nonlinear optical devices utilizing two-dimensional (2D) materials and nanostructures are emerging as important elements of photonic circuits. Integration of the nonlinear material into a subwavelength cavity or waveguide leads…
Nonlinear spectroscopy is widely used to study the transient dynamics of molecules under strong light-matter coupling, though it remains unclear to what extent uncoupled intracavity molecules obscure signals from the strongly-coupled…
We demonstrate full integration of vertical optical ring resonators with silicon nanophotonic waveguides on silicon-on-insulator substrates to accomplish a significant step towards 3D photonic integration. The on-chip integration is…
One of the main problems that optical quantum computing has to overcome is the efficient construction of two-photon gates. Theoretically these gates can be realized using Kerr-nonlinearities, but the techniques involved are experimentally…
Optical computing systems provide an alternate hardware model which appears to be aligned with the demands of neural network workloads. However, the challenge of implementing energy efficient nonlinearities in optics -- a key requirement…
Quantum Reservoir Computing (QRC) harnesses quantum systems to tackle intricate computational problems with exceptional efficiency and minimized energy usage. This paper presents a QRC framework that utilizes a minimalistic quantum…
We review some of the recent concepts and their realization exploiting the perfect destructive interference of light in micro and nano structures. One refers to optical structures where the effective absorption can be controlled and…