Recent years have seen the rapid growth and development of the field of smart photonics, where machine learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics -the generation and characterization of light pulses, the study of light-matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

Materials with electrically tunable optical properties offer a wide range of opportunities for photonic applications. The optical properties of the single-walled carbon nanotubes (SWCNTs) can be significantly altered in the near-infrared region by means of electrochemical doping. The states’ filling, which is responsible for the optical absorption suppression under doping, also alters the nonlinear optical response of the material. Here, for the first time we report that the electrochemical doping can tailor the nonlinear optical absorption of SWCNT films and demonstrate its application to control pulsed fiber laser generation. With a pump–probe technique, we show that under an applied voltage below 2 V the photobleaching of the material can be gradually reduced and even turned to photoinduced absorption. Furthermore, we integrated a carbon nanotube electrochemical cell on a side-polished fiber to tune the absorption saturation and implemented it into the fully polarization-maintaining fiber laser. We show that the pulse generation regime can be reversibly switched between femtosecond mode-locking and microsecond Q-switching using different gate voltages. This approach paves the road toward carbon nanotube optical devices with tunable nonlinearity.

The Letter proposes a new layout of a passively mode-locked fiber laser based on a nonlinear amplifying loop mirror (NALM) with two stretches of active fiber and two independently controlled pump modules. In contrast with conventional NALM configurations using a single piece of active fiber that yields virtually constant peak power, the proposed novel laser features larger than a factor of 2 adjustment range of peak power of generated pulses. The proposed layout also provides independent adjustment of duration and peak power of generated pulses as well as power-independent control of generated pulse spectral width impossible in NALM lasers with a single piece of active fiber.

This work for the first time reports the results on study of a polymer-free carbon nanotube (CNT) films used as a saturable absorber in an all-fibre laser. It is demonstrated that free-standing single-walled CNT films fabricated by an aerosol method are able to ensure generation of transform-limited pulses in an Er all-fibre ring laser with duration of several picoseconds and high quality of mode locking. The optimal average output power levels are identified, amounting to 0.4-0.5 mW depending on the linear transmission of the studied samples (60% or 80%). Application of polymer-free CNT films solves problems related to degradation of conventional polymer matrices of CNT-based saturable absorbers and paves the way to longer-lasting and more reliable saturable absorbers compatible with all-fibre laser configurations.

Many types of modern lasers feature nonlinear properties, which makes controlling their operation a challenging engineering problem. In particular, fibre lasers present both high-performance devices that are already used for diverse industrial applications, but also interesting and not yet fully understood nonlinear systems. Fibre laser systems operating at high power often have multiple equilibrium states, and this produces complications with the reproducibility and management of such devices. Self-tuning and feedback-enabled machine learning approaches might define a new era in laser science and technology. The present study is the first to demonstrate experimentally the application of machine learning algorithms for control of the pulsed regimes in an all-normal dispersion, figure-eight fibre laser with two independent amplifying fibre loops. The ability to control the laser operation state by electronically varying two drive currents makes this scheme particularly attractive for implementing machine learning approaches. The self-tuning adjustment of two independent gain levels in the laser cavity enables generation-on-demand pulses with different duration, energy, spectral characteristics and time coherence. We introduce and evaluate the application of several objective functions related to selection of the pulse duration, energy and degree of temporal coherence of the radiation. Our results open up the possibility for new designs of pulsed fibre lasers with robust electronics-managed control.