Logan G Wright

• Professor (Assistant) at Yale University

As optical fiber communications and fiber lasers approach fundamental limits there is considerable interest in multimode fibers. In nonlinear science, they represent an exciting environment for complex nonlinear waves. As in single-mode fiber, solitons may be particularly important. Multimode solitons consist of synchronized, non-dispersive pulses...

Multimode fibres are of interest for next-generation telecommunications systems and the construction of high-energy fibre lasers. However, relatively little work has explored nonlinear pulse propagation in multimode fibres. Here, we consider highly nonlinear ultrashort pulse propagation in the anomalous-dispersion regime of a graded-index multimode...

Though new affordable high power laser technologies make possible many processing applications in science and industry, depth control remains a serious technical challenge. Here we show that inline coherent imaging, with line rates up to 312 kHz and microsecond-duration capture times, is capable of directly measuring laser penetration depth in a pr...

Multimode fibres (MMFs) are attracting interest for complex spatiotemporal dynamics, and for ultrafast fibre sources, imaging and telecommunications. This new interest is based on three key properties: their high spatiotemporal complexity (information capacity), the important role of disorder, and complex intermodal interactions. To date, phenomena...

We experimentally isolate and directly observe multimode solitons in few-mode graded-index fiber. We rely on Raman frequency shifts to spectrally isolate these multimode solitons. By varying the input energy and modal composition of the launched pulse, we observe a continuous variation of multimode solitons with different spatiotemporal properties....

A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with $>400$ modes as the reservoir in the quantum-machine-learning approach of quantum reservoir computing. We evaluated the accuracy of our GBS-ba...

Nonlinear photonics uses coherent interactions between optical waves to engineer functionality that is not possible with purely linear optics. Traditionally, the function of a nonlinear-optical device is determined during design and fixed during fabrication. In this paper, we present a photonic device with highly programmable nonlinear functionalit...

Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio...

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on microwave superconducti...

Over the last few decades, nonlinear optics has become significantly more nonlinear, traversing nearly a billionfold improvement in energy efficiency, with ultrafast nonlinear nanophotonics in particular emerging as a frontier for combining both spatial and temporal engineering. At present, cutting-edge experiments in nonlinear nanophotonics place...

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x l...

We introduce a device containing a planar waveguide whose spatial refractive index profile n ( x , z ) can be programmed in real time. We demonstrate use this device as an optical neural network.

We demonstrate a source of highly multimode, high gain vacuum squeezed light, and demonstrate how ultra-broadband frequency conversion enables the use of visible-light detector arrays and programmable frequency-domain unitary transformations.

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents a major goal of the everlasting pursue of ultra-fast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatio...

Analog physical neural networks, which hold promise for improved energy efficiency and speed compared to digital electronic neural networks, are nevertheless typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10). What happens if an analog system is instead operated in an ultra-low-power regime,...

Photonic simulators using synthetic frequency dimensions have enabled flexible experimental analogues of condensed-matter systems. However, so far, such photonic simulators have been limited in scale, yielding results that suffer from finite-size effects. Here we present an analogue simulator capable of simulating large two-dimensional (2D) and 3D...

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 or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that—instead of performing imaging—ac...

The rapidly increasing size of deep-learning models has caused renewed and growing interest in alternatives to digital computers to dramatically reduce the energy cost of running state-of-the-art neural networks. Optical matrix-vector multipliers are best suited to performing computations with very large operands, which suggests that large Transfor...

We present a programmable analog simulator with up to 10 ⁵ lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary lattice excitations.

We use a multilayer, nonlinear optical neural network as a pre-processor for machine vision. On tasks including identification o f r eal o bjects i lluminated by broad-band incoherent light, we show that nonlinear optical pre-processing outperforms linear pre-processing.

We pushed the optical energy consumption of optical neural networks to a new regime. Despite dominant quantum noise, we experimentally achieved accurate image classification using 0.008 photons/MAC, demonstrating deterministic machine-learning tasks with ultra-low-power stochastic systems.

We use a multilayer, nonlinear optical neural network (ONN) as a pre-processor for machine vision. On tasks including identification of real objects illuminated by broadband incoherent light, we show that nonlinear optical pre-processing outperforms linear pre-processing.

We use a multilayer, nonlinear optical neural network (ONN) as a pre-processor for machine vision. On tasks including identification of real objects illuminated by broadband incoherent light, we show that nonlinear optical pre-processing outperforms linear pre-processing.

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents the holy grail of the human everlasting pursue of ultrafast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally,...

Linear multimode optical systems have enabled clean experimental observations and the applications of numerous phenomena that continually extend the boundaries of wave physics. The infrastructure that has enabled these studies facilitates the study of an even richer world of nonlinear multimode optical systems. Multimode nonlinear optical physics i...

Photonic simulators using synthetic frequency dimensions have enabled flexible experimental analogues of condensed-matter systems, realizing phenomena that are impractical to observe in real-space systems. However, to date such photonic simulators have been limited to small systems suffering from finite-size effects. Here, we present an analog simu...

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...

The overall goal of photonics research is to understand and control light in new and richer ways to facilitate new and richer applications. Many major developments to this end have relied on nonlinear optical techniques, such as lasing, mode-locking, and parametric downconversion, to enable applications based on the interactions of coherent light w...

Nonlinear multimode optical systems support a host of intriguing effects that are impossible in single-mode settings. Although nonlinearity can provide a rich environment where the chaotic power exchange among thousands of modes can lead to novel behaviours, understanding and harnessing these processes to our advantage is challenging. Over the year...

We study the emergence of non-Gaussian quantum features in pulsed squeezed light generation with a mesoscopic number (i.e., dozens to hundreds) of pump photons. Due to the strong optical nonlinearities necessarily involved in this regime, squeezing occurs alongside significant pump depletion, compromising the predictions made by conventional semicl...

Recent years have witnessed a resurgence of interest in nonlinear multimode optical systems where a host of intriguing effects have been observed that are impossible in single-mode settings. While nonlinearity can provide a rich environment where the chaotic power exchange among thousands of modes can lead to novel behaviors, at the same time, it p...

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability ¹ . Deep-learning accelerators 2–9 aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics...

Deep learning has become a widespread tool in both science and industry. However, continued progress is hampered by the rapid growth in energy costs of ever-larger deep neural networks. Optical neural networks provide a potential means to solve the energy-cost problem faced by deep learning. Here, we experimentally demonstrate an optical neural net...

We have shown that nonlinear pulse propagation can be designed to directly, all-optically implement machine learning calculations. Building from proof-of-concept experiments, we analyze opportunities for ultrafast, ultra-efficient nonlinear optical neuromorphic systems and smart sensors.

We realize efficient full-quantum simulations of pulse propagation in highly nonlinear waveguides using matrix product states. As a demonstration, we study the quan-tum dynamics of an optical soliton, highlighting the emergence of non-Gaussian quantum features.

We demonstrate a multilayer optical-neural-network image encoder realized using optoelectronic, optical-to-optical nonlinear activations. We show that nonlinear preprocessing in the optical domain can enable machine-vision systems that operate faster and more sensitively.

We experimentally demonstrate multilayer neural networks using ultrafast nonlinear optics, to perform audio and image classification. The proposed framework for constructing and training neural networks is general and applicable to other complex non-linear systems.

We study the emergence of non-Gaussian quantum features in pulsed squeezed light generation with a mesoscopic number (i.e., dozens to hundreds) of pump photons. Due to the strong optical nonlinearities necessarily involved in this regime, squeezing occurs alongside significant pump depletion, compromising the predictions made by conventional semicl...

Ultrashort pulses propagating in nonlinear nanophotonic waveguides can simultaneously leverage both temporal and spatial field confinement, promising a route towards single-photon nonlinearities in an all-photonic platform. In this multimode quantum regime, however, faithful numerical simulations of pulse dynamics naïvely require a representation o...

Deep learning has rapidly become a widespread tool in both scientific and commercial endeavors. Milestones of deep learning exceeding human performance have been achieved for a growing number of tasks over the past several years, across areas as diverse as game-playing, natural-language translation, and medical-image analysis. However, continued pr...

Deep neural networks have become a pervasive tool in science and engineering. However, modern deep neural networks' growing energy requirements now increasingly limit their scaling and broader use. We propose a radical alternative for implementing deep neural network models: Physical Neural Networks. We introduce a hybrid physical-digital algorithm...

Ultra-short pulses propagating in nonlinear nanophotonic waveguides can simultaneously leverage both temporal and spatial field confinement, promising a route towards single-photon nonlinearities in an all-photonic platform. In this multimode quantum regime, however, faithful numerical simulations of pulse dynamics naïvely require a representation...

We experimentally demonstrate deep nonlinear optical neural networks using a universal algorithm for backpropagating through arbitrary physical input-output transformations. Ultrafast second harmonic generation and other diverse processes are trained to perform image and audio classification.

We report an experimental demonstration of an optical neural network performing image classification with high accuracy using less than a single photon per scalar multiplication, validating theoretical predictions about quantum-limited performance of ONNs.

We experimentally demonstrate an optical neural network whose fundamental building blocks are controllable ultrafast quadratic nonlinear pulse-propagation processes. The network is trained in situ by a new backpropagation algorithm that generalizes to arbitrary physical systems.

Unexpected multimode solitary waves can be formed spontaneously in hollow-core fibres, hinting at a vast world of exciting nonlinear optics, with applications for generating few-cycle, ultra-intense pulses.

We propose a deterministic, measurement-free implementation of a cubic phase gate for continuous- variable quantum information processing. In our scheme, the applications of displacement and squeezing operations allow us to engineer the effective evolution of the quantum state propagating through an optical Kerr nonlinearity. Under appropriate cond...

Mode-locking is a process in which different modes of an optical resonator establish stable synchronization through non-linear interactions. This self-organization underlies light sources that enable many modern scientific applications, such as ultrafast and high-field optics and frequency combs. Despite this, mode-locking has almost exclusively re...

We propose a measurement-free construction of a cubic phase gate based on a Kerr nonlinearity and Gaussian transformations. Experimental feasibility is discussed for pulsed nanophotonic waveguides where quantum states are encoded into quantum solitons.

Quantum neural networks (QNN) are a promising application of near-term quantum computers. We present an information theory of QNN’s expressive power, which we apply to an example optical QNN based on a Gaussian Boson Sampler.

We uncover the mechanisms behind a range of new 3D solitons in multimode lasers using an approach called attractor dissection. Experimental measurements allow us to decompose multimode states containing up to 30 million locked modes.

We propose a deterministic, measurement-free implementation of a cubic phase gate for continuous-variable quantum information processing. In our scheme, the applications of displacement and squeezing operations allow us to engineer the effective evolution of the quantum state propagating through an optical Kerr nonlinearity. Under appropriate condi...

Mode-locking is a process in which different modes of an optical resonator establish, through nonlinear interactions, stable synchronization. This self-organization underlies light sources that enable many modern scientific applications, such as ultrafast and high-field optics and frequency combs. Despite this, mode-locking has almost exclusively r...

A key open question in quantum computation is what advantages quantum neural networks (QNNs) may have over classical neural networks (NNs), and in what situations these advantages may transpire. Here we address this question by studying the memory capacity $C$ of QNNs, which is a metric of the expressive power of a QNN that we have adapted from cla...

We outline a theoretical framework to understand the multitude of new mode-locked states possible in multi-transverse mode resonators. Full-3D measurements of mode-locked states comprising roughly 30 million modes agree with theoretical expectations.

We demonstrate a fiber oscillator that achieves 3 MW peak power, is easily started, and is environmentally stable. The Mamyshev oscillator delivers 190-nJ pulses that can be compressed externally to 35 fs duration. Accurate numerical modeling of the gain medium provides insight into the behavior and performance of the device.

Ultrafast fiber lasers have the potential to make applications of ultrashort pulses widespread – techniques not only for scientists, but also for doctors, manufacturing engineers, and more. Today, this potential is only realized in refractive surgery and some femtosecond micromachining. The existing market for ultrafast lasers remains dominated by...

We demonstrate a fiber oscillator that achieves 3 MW peak power, is easily started and is environmentally stable. The Mamyshev oscillator delivers 190-nJ pulses that can be compressed externally to 35 fs duration. Accurate numerical modeling of the gain medium provides insight into the behavior and performance of the device.

We find adiabatic four-wave mixing in optical fibers allows efficient, near-octave-spanning near-infrared to mid-infrared conversion. Simulations indicate several possible fiber platforms, extending one-to-one broadband frequency conversion both to high-repetition-rate and high-energy applications.

We demonstrate an environmentally stable, self-seeded fiber oscillator based on spectral reshaping and reamplification. The oscillator delivers 140 nJ, linearly chirped pulses with 110 nm spectral bandwidth that can be compressed externally to 65 fs.

We demonstrate use of spatial light modulators to control modal excitation and make mode-resolved measurements of nonlinear pulse propagation in multimode fiber, and present a representative experiment wherein we observe discrete Raman beam clean-up.

We describe, through multiple theoretical and experimental realizations, spatiotemporal mode-locking (STML), the most general form of self-organization in optical oscillators. We discuss qualitatively new kinds of mode-locking, and routes to ultrahigh power, compact lasers.

We demonstrate a fiber oscillator that is environmentally stable and self-seeded. The oscillator generates 190 nJ, linearly chirped pulses that can be compressed to 35 fs resulting in 3 MW peak power.

Using spatial light modulators, we demonstrate control of modal excitation and mode-resolved measurement of nonlinear pulse propagation in multimode fiber, and present a representative experiment in which we observe discrete Raman beam clean-up.

We describe, in general terms, what spatiotemporal mode-locking is and how it happens. Then we describe several recent new developments, including qualitatively distinct kinds of 3D pulses and mode-locking physics.

Building on the scientific understanding and technological infrastructure of single-mode fibers, multimode fibers are being explored as a means of adding new degrees of freedom to optical technologies such as telecommunications, fiber lasers, imaging, and measurement. Here, starting from a baseline of single-mode nonlinear fiber optics, we introduc...

Building on the scientific understanding and technological infrastructure of single-mode fibers, multimode fibers are being explored as a means of adding new degrees of freedom to optical technologies such as telecommunications, fiber lasers, imaging, and measurement. Here, starting from a baseline of single-mode nonlinear fiber optics, we introduc...

We demonstrate a fiber system that amplifies and compresses pulses from a gain-switched diode. A Mamyshev regenerator shortens the pulses and improves their coherence, enabling subsequent amplification by parabolic pre-shaping. As a result, we are able to control nonlinear effects and generate nearly transform-limited 140-fs pulses with 13-MW peak...

Fiber lasers that generate ultrashort light pulses can offer practical advantages over solid-state lasers for some applications. However, the achievement of high peak power with environmentally stable designs remains a major challenge for fiber oscillators. We demonstrate that an environmentally stable source based on cascaded Mamyshev regeneration...

Harnessing complexity in laser light The development of lasers and the quality of the output light has been crucially dependent on understanding and being able to control the process occurring within the laser-generating cavity. In a real laser cavity, there are both longitudinal and transverse modes; for the highest-quality lasers, reducing the ef...

A laser is based on the electromagnetic modes of its resonator, which provides the feedback required for oscillation. Enormous progress has been made in controlling the interactions of longitudinal modes in lasers with a single transverse mode. For example, the field of ultrafast science has been built on lasers that lock many longitudinal modes to...

We demonstrate a fiber system which amplifies and compresses pulses from a gain-switched diode. A Mamyshev regenerator shortens the pulses and improves their coherence, enabling subsequent amplification by parabolic pre-shaping. As a result, we are able to control nonlinear effects and generate nearly transform-limited, 140-fs pulses with 13-MW pea...

We demonstrate that the pump’s spatial input profile can provide additional degrees of freedom in tailoring at will the nonlinear dynamics and the ensuing spectral content of supercontinuum generation in highly multimoded optical fibers. Experiments and simulations carried out at 1550 nm indicate that the modal composition of the input beam can sub...

We demonstrate a fiber source with the best performance from an ultrafast fiber oscillator to date. The ring-cavity Mamyshev oscillator produces 50-nJ and 40-fs pulses. The peak power is an order of magnitude higher than that of previous lasers with similar fiber mode area. This performance is achieved by designing the oscillator to support parabol...

We observe the self-organization of light into its most spatiotemporally-unstable state through propagation in graded-index multimode fiber. We understand this effect in terms of mode-coupling caused by dissipation, disorder, and nonlinearity.