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

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

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 ($\exp(-\alpha_\textrm{DW} N^2)$) relative to CIMs ($\exp(-\alpha_\textrm{CIM} N)$) for fixed anneal times, on both the SK model and on 50%-edge-density MAX-CUT, where the coefficients $\alpha_\textrm{CIM}$ and $\alpha_\textrm{DW}$ are problem-class-dependent. On instances with over $50$ 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

Scaling advantages of all-to-all connectivity in physical annealers: the Coherent Ising Machine vs. D-Wave 2000Q

Physical annealing systems provide a heuristic approach to solve NP-hard Ising optimization problems. It is believed that the connectivity between spins in such annealers significantly impacts the machine's computational effectiveness. In this paper we study the performance of two types of annealing machines that have very different connectivity -- a commercially available quantum annealer built by D-wave Systems, which has sparse connectivity, and coherent Ising machines based on optical parametric oscillator networks, which have all-to-all connectivity. We demonstrate an exponential (e^(−O(N^2))) penalty in performance for the D-wave quantum annealer relative to coherent Ising machines when solving Ising problems on dense graphs, which is attributable to the differences in internal connectivity between the machines. This leads to a several-orders-of-magnitude time-to-solution difference between coherent Ising machines and the D-wave system for problems with over 50 vertices. Our results provide strong experimental support to efforts to increase the connectivity of physical annealers

Experimental investigation of performance differences between coherent Ising machines and a quantum annealer

Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines—a quantum annealer built by D-Wave Systems and measurement-feedback coherent Ising machines (CIMs) based on optical parametric oscillators—on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(–α_(DW)N^2)] relative to CIMs [exp(–α_(CIM)N)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal–annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers