Microwave signal processing using an analog quantum reservoir computer.

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 superconducting circuits to classify microwave signals that are analog-continuous in time. However, while there have been theoretical proposals of analog QRC, to date QRC has been implemented using the circuit model-imposing a discretization of the incoming signal in time.

The hardware is the software.

Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the physics of artificial intelligence hardware and of human biological "hardware" is distinct, neuromorphic engineers need to be selective in the inspiration we take from neuroscience.

Quantum-noise-limited optical neural networks operating at a few quanta per activation.

A practical limit to energy efficiency in computation is ultimately from noise, with quantum noise [1] as the fundamental floor. Analog physical neural networks [2], 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). We study optical neural networks [3] operated in the limit where all layers except the last use only a single photon to cause a neuron activation.

Deep physical neural networks trained with backpropagation.

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

An optical neural network using less than 1 photon per multiplication.

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.

Engineering a Kerr-Based Deterministic Cubic Phase Gate via Gaussian Operations.

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 conditions, we show that the input state evolves according to a cubic phase Hamiltonian, and we find that the cubic phase gate error decreases inverse quartically with the amount of quadrature squeezing, even in the presence of linear loss.

Self-seeded, multi-megawatt, Mamyshev oscillator.

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.

Several new directions for ultrafast fiber lasers [Invited].

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 solid-state lasers, primarily Ti:sapphire, due to their superior performance.

High-power femtosecond pulses without a modelocked laser.

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 peak power-an order-of-magnitude improvement over previous gain-switched diode sources.

Spatiotemporal mode-locking in multimode fiber lasers.

A laser is based on the electromagnetic modes of its resonator, which provides the feedback required for oscillation. Enormous progress has been made toward 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 together to form ultrashort light pulses.

Megawatt peak power from a Mamyshev oscillator.

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.

Observation of multimode solitons in few-mode fiber.

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.

Kerr self-cleaning of femtosecond-pulsed beams in graded-index multimode fiber.

We observe a nonlinear spatial self-cleaning process for femtosecond pulses in graded-index (GRIN) multimode fiber (MMF). Pulses with ∼80 fs duration at 1030 nm are launched into GRIN MMF with 62.5 μm core.

Ultrabroadband Dispersive Radiation by Spatiotemporal Oscillation of Multimode Waves.

In nonlinear dynamical systems, qualitatively distinct phenomena occur depending continuously on the size of the bounded domain containing the system. For nonlinear waves, a multimode waveguide is a bounded three-dimensional domain, allowing observation of dynamics impossible in open settings. Here we study radiation emitted by bounded nonlinear waves: the spatiotemporal oscillations of solitons in multimode fiber generate multimode dispersive waves over an ultrabroadband spectral range.

Ultrafast fiber lasers based on self-similar pulse evolution: a review of current progress.

Self-similar fiber oscillators are a relatively new class of mode-locked lasers. In these lasers, the self-similar evolution of a chirped parabolic pulse in normally-dispersive passive, active, or dispersion-decreasing fiber (DDF) is critical. In active (gain) fiber and DDF, the novel role of local nonlinear attraction makes the oscillators fundamentally different from any mode-locked lasers considered previously.

Spatiotemporal dynamics of multimode optical solitons.

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.

Divided-pulse lasers.

We demonstrate the use of coherent division and recombination of the pulse within an ultrafast laser cavity to manage the nonlinear phase accumulation and scale the output pulse energy. We implement the divided-pulse technique in an ytterbium-doped fiber laser and achieve 16 times scaling of the pulse energy, to generate 6 nJ and 1.4 ps solitons in single-mode fiber.

Deep nonlinear ablation of silicon with a quasi-continuous wave fiber laser at 1070 nm.

We achieve high aspect-ratio laser ablation of silicon with a strong nonlinear dependence on pulse duration while using a power density 10(6) times less than the threshold for typical multiphoton-mediated ablation. This is especially counter-intuitive as silicon is nominally transparent to the modulated continuous wave Yb:fiber laser used in the experiments. We perform time-domain finite-element simulations of thermal dynamics to investigate thermo-optical coupling and link the observed machining to an intensity-thresholded runaway thermo-optically nonlinear process.