21 research outputs found
Nonlinear Quantum Behavior of Ultrashort-Pulse Optical Parametric Oscillators
The quantum features of ultrashort-pulse optical parametric oscillators (OPOs) are theoretically investigated in the nonlinear regime near and above threshold. Starting from basic premises of input-output theory, we derive a general quantum model for pulsed OPOs subject to χ(2) interactions between a multimode signal cavity and a non-resonant broadband pump field, elucidating time scale conditions required for such pulsed OPOs to admit an input-output description. By employing a supermode decomposition of the nonlinear Lindblad operators governing pump-signal interactions, we perform multimode quantum simulations in the regime of strong nonlinearity and study effects such as pump depletion and corrections to the squeezing spectrum of the linearized model. We observe non-Gaussian states with Wigner function negativity and show that multimode interactions with the pump can act as decoherence channels
Nonlinear Quantum Behavior of Ultrashort-Pulse Optical Parametric Oscillators
The quantum features of ultrashort-pulse optical parametric oscillators (OPOs) are theoretically investigated in the nonlinear regime near and above threshold. Starting from basic premises of input-output theory, we derive a general quantum model for pulsed OPOs subject to χ(2) interactions between a multimode signal cavity and a non-resonant broadband pump field, elucidating time scale conditions required for such pulsed OPOs to admit an input-output description. By employing a supermode decomposition of the nonlinear Lindblad operators governing pump-signal interactions, we perform multimode quantum simulations in the regime of strong nonlinearity and study effects such as pump depletion and corrections to the squeezing spectrum of the linearized model. We observe non-Gaussian states with Wigner function negativity and show that multimode interactions with the pump can act as decoherence channels
Highly multimode visible squeezed light with programmable spectral correlations through broadband up-conversion
Multimode squeezed states of light have been proposed as a resource for
achieving quantum advantage in computing and sensing. Recent experiments that
demonstrate multimode Gaussian states to this end have most commonly opted for
spatial or temporal modes, whereas a complete system based on frequency modes
has yet to be realized. Instead, we show how to use the frequency modes
simultaneously squeezed in a conventional, single-spatial-mode, optical
parametric amplifier when pumped by ultrashort pulses. Specifically, we show
how adiabatic frequency conversion can be used not only to convert the quantum
state from infrared to visible wavelengths, but to concurrently manipulate the
joint spectrum. This near unity-efficiency quantum frequency conversion, over a
bandwidth >45 THz and, to our knowledge, the broadest to date, allows us to
measure the state with an electron-multiplying CCD (EMCCD) camera-based
spectrometer, at non-cryogenic temperatures. We demonstrate the squeezing of
>400 frequency modes, with a mean of approximately 700 visible photons per
shot. Our work shows how many-mode quantum states of light can be generated,
manipulated, and measured with efficient use of hardware resources -- in our
case, using one pulsed laser, two nonlinear crystals, and one camera. This
ability to produce, with modest hardware resources, large multimode squeezed
states with partial programmability motivates the use of frequency encoding for
photonics-based quantum information processing
Image sensing with multilayer, nonlinear optical neural networks
Optical imaging is commonly used for both scientific and technological
applications across industry and academia. In image sensing, a measurement,
such as of an object's position, is performed by computational analysis of a
digitized image. An emerging image-sensing paradigm breaks this delineation
between data collection and analysis by designing optical components to perform
not imaging, but encoding. By optically encoding images into a compressed,
low-dimensional latent space suitable for efficient post-analysis, these image
sensors can operate with fewer pixels and fewer photons, allowing
higher-throughput, lower-latency operation. Optical neural networks (ONNs)
offer a platform for processing data in the analog, optical domain. ONN-based
sensors have however been limited to linear processing, but nonlinearity is a
prerequisite for depth, and multilayer NNs significantly outperform shallow NNs
on many tasks. Here, we realize a multilayer ONN pre-processor for image
sensing, using a commercial image intensifier as a parallel optoelectronic,
optical-to-optical nonlinear activation function. We demonstrate that the
nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while
still enabling high accuracy across several representative computer-vision
tasks, including machine-vision benchmarks, flow-cytometry image
classification, and identification of objects in real scenes. In all cases we
find that the ONN's nonlinearity and depth allowed it to outperform a purely
linear ONN encoder. Although our experiments are specialized to ONN sensors for
incoherent-light images, alternative ONN platforms should facilitate a range of
ONN sensors. These ONN sensors may surpass conventional sensors by
pre-processing optical information in spatial, temporal, and/or spectral
dimensions, potentially with coherent and quantum qualities, all natively in
the optical domain
Scaling on-chip photonic neural processors using arbitrarily programmable wave propagation
On-chip photonic processors for neural networks have potential benefits in
both speed and energy efficiency but have not yet reached the scale at which
they can outperform electronic processors. The dominant paradigm for designing
on-chip photonics is to make networks of relatively bulky discrete components
connected by one-dimensional waveguides. A far more compact alternative is to
avoid explicitly defining any components and instead sculpt the continuous
substrate of the photonic processor to directly perform the computation using
waves freely propagating in two dimensions. We propose and demonstrate a device
whose refractive index as a function of space, , can be rapidly
reprogrammed, allowing arbitrary control over the wave propagation in the
device. Our device, a 2D-programmable waveguide, combines photoconductive gain
with the electro-optic effect to achieve massively parallel modulation of the
refractive index of a slab waveguide, with an index modulation depth of
and approximately programmable degrees of freedom. We used a
prototype device with a functional area of to perform
neural-network inference with up to 49-dimensional input vectors in a single
pass, achieving 96% accuracy on vowel classification and 86% accuracy on -pixel MNIST handwritten-digit classification. This is a scale beyond
that of previous photonic chips relying on discrete components, illustrating
the benefit of the continuous-waves paradigm. In principle, with large enough
chip area, the reprogrammability of the device's refractive index distribution
enables the reconfigurable realization of any passive, linear photonic circuit
or device. This promises the development of more compact and versatile photonic
systems for a wide range of applications, including optical processing, smart
sensing, spectroscopy, and optical communications
Experimental investigation of performance differences between Coherent Ising Machines and a quantum annealer
Physical annealing systems provide heuristic approaches to solving NP-hard
Ising optimization problems. Here, we study the performance of two types of
annealing machines--a commercially available quantum annealer built by D-Wave
Systems, and measurement-feedback coherent Ising machines (CIMs) based on
optical parametric oscillator networks--on two classes of problems, the
Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer
outperforms the CIMs on MAX-CUT on regular graphs of degree 3. On denser
problems, however, we observe an exponential penalty for the quantum annealer
() relative to CIMs () for fixed anneal times, on both the SK model and on 50%-edge-density
MAX-CUT, where the coefficients and
are problem-class-dependent. On instances with over vertices, a
several-orders-of-magnitude time-to-solution difference exists between CIMs and
the D-Wave annealer. An optimal-annealing-time analysis is also consistent with
a significant projected performance difference. The difference in performance
between the sparsely connected D-Wave machine and the measurement-feedback
facilitated all-to-all connectivity of the CIMs provides strong experimental
support for efforts to increase the connectivity of quantum annealers.Comment: 12 pages, 5 figures, 1 table (main text); 14 pages, 12 figures, 2
tables (supplementary