We theoretically investigate a method for tuning a dissipation rate in an atom–cavity system. By inserting a nanotip into the cavity mode, we estimate that the cavity dissipation rate increases by a factor of ∼20 due to the scattering and absorption losses at the tip. As applications of our in situ technique, we demonstrate that an exceptional line can be obtained when a single atom or ion is coupled to the resonator. Moreover, the position of an exceptional point is tuned by adjusting the decay rate, enabling a topological transition through dissipation control alone. Our study establishes a scattering-induced dissipation channel for probing the cavity field and enables controlled quantum state switching in cavity quantum electrodynamics.

Scattering imaging techniques are significant tools in biomedicine and underwater detection. Speckle cross correlation imaging of a moving object makes it possible to reconstruct hidden objects through random media. The cross correlation imaging is difficult to apply in dynamic scattering media and requires a moving object. We propose a method based on the far-field transmission of a mutual coherence function to reconstruct the image of object obscured by dynamic scattering media. We characterize the imaging capability through the Euclidean distance and analyze the impact of two system parameters on the Euclidean distance. Subsequently, object images of different sizes were reconstructed. This work provides an innovative applicable method for non-invasive optical imaging.

Photonic devices greatly benefit from the ability to reconfigure their optical properties. To achieve this with disordered optical materials, it is possible to make use of polymer dispersed liquid crystals (PDLCs) in which the scattering originates from randomly distributed liquid crystal droplets that respond to external stimuli, such as light, temperature, and magnetic or electric fields. In this work, we present a speckle-based analysis to investigate the reconfigurability of the dynamic and static scattering properties of dye-doped PDLCs, as a function of dye concentration and light intensity. Our findings allow us to distinguish between reversible and non-reversible molecular rearrangements after a light-induced phase transition, occurring on millisecond timescale. The degree of reversibility is quantified using cross-correlation analysis based on the Pearson correlation coefficient. As a proof-of-concept application, we exploit their non-reversible reconfiguration properties to create a cryptographic optical physical unclonable function whose behavior can progressively be erased in case of malicious attacks. We demonstrate a non-reversibility, assessed through the uniqueness metric, that ranges from 30% to 40% depending on dye content. Leveraging their intensity-dependent scattering properties, these disordered materials emerge as ideal candidates for advanced nonlinear information processing and secret-free security applications.

Methods for simultaneously controlling the spatial, spectral, and temporal properties of ultrashort pulses after propagation through a disordered multimode fiber are presented. By leveraging the dispersion and strong mode-mixing within a disordered fiber, multidimensional control over the output pulse is achieved by tailoring only the spatial wavefront of the input pulse. The ability to generate reconfigurable spatiotemporal foci at single or multiple points in space and time is demonstrated experimentally. The control is extended to the spectral domain, as demonstrated by the successful creation of spectrally-dependent foci, with different wavelength bands focused to distinct spatial locations and times. An approach for time-averaged wavefront shaping is also introduced and is shown to enable the creation of complex spatial intensity patterns with tailored properties.

We demonstrate the direct locking of a 820 kHz linewidth semiconductor distributed feedback laser to a high finesse, compact, and ultrastable reference cavity using the optoelectronic (OEO) laser locking method, resulting in cavity thermal noise-limited phase noise performance. We compare the OEO lock to the standard Pound–Drever–Hall method, demonstrating several new aspects and advantages of the OEO lock in attaining low phase noise from noisy lasers, including reducing the residual laser noise by more than 140 dB compared to its free-running level at a 10 Hz offset. In addition, we introduce a new RF feedforward noise-correction scheme that transfers the stability of the ultrastable reference cavity to an optical frequency comb even if the cavity-stabilized laser itself has high residual noise. We also reveal an OEO lock state that is capable of extremely high levels of laser noise rejection using feedforward, exceeding current theoretical models by ∼15 dB. By combining compact laser sources with sub-1 ml volume and ultrastable optical cavities, this work enables extremely compact and robust ultrastable laser systems with applications in low phase noise microwave generation, sensing, and satellite ranging.

The use of C-band wavelengths in the field of quantum communication has grown significantly, driving the need for versatile detection solutions, especially in the low intensity domain. Among the desirable features for such detectors, photon-number-resolving (PNR) capability is particularly valuable, since it can offer new possibilities for enhancing security of communication protocols. In this paper, we present the implementation of a receiver that combines low-cost PNR detectors with nonlinear optical interactions to achieve sensitivity at telecom wavelengths. In particular, we use this receiver to characterize the Poissonian nature of a femtosecond source at 1.5 μm, produced via white light continuum generation followed by a single-stage amplification process. The obtained results encourage the exploitation of such a detector in more complex schemes.

The growing computational demands of artificial intelligence motivate the development of optical neural networks (ONNs), while their practical deployment remains constrained by optimization complexity and limited adaptability. We propose a mutual learning knowledge distillation framework for optical diffractive neural networks (ODNNs), synergizing photonic computing with adaptive training strategies. Our architecture enables bidirectional knowledge transfer between parameter-efficient optical student networks and electrical ResNet teachers, leveraging both labeled data and probabilistic guidance. The validation on Fashion-MNIST and Kuzushiji-MNIST demonstrates classification accuracy improvements of 5.9% and 13.88%, respectively, over conventional ODNNs training. In addition, we further developed the photonic to photonic mutual learning computational paradigm, in which an optical network with 0.98 × 106 parameters can bootstrap compact photonic classifiers with 0.19 × 106 parameters, achieving an accuracy of 79.5% on MNIST and yielding an absolute improvement of 9% relative to the noncooperative baseline. Even though the all-optical mutual learning paradigm still has a gap of about 19% with the state-of-the-art ODNNs, the number of parameters of the guided student networks is smaller than the state-of-the-art networks by several or even dozens of times. These results establish a paradigm for overcoming fundamental limitations in ONN optimization, paving the way for a new era in which ONNs can solve large-scale practical problems through photonic–electronic and photonic–photonic co-design.

Nonlinear interferometry has widespread applications in sensing, spectroscopy, and imaging. However, most implementations require highly reflective mirrors and precise optical alignment, drastically reducing their versatility and usability in outdoor applications. This work is based on stimulated parametric downconversion, demonstrating methane absorption spectroscopy in the mid-infrared (MIR) region by detecting near-infrared photons using a silicon-based CMOS camera. The MIR light, used to probe methane, is diffusely backscattered from a Lambertian surface, experiencing significant transmission loss. We implement a single-mode confocal illumination and collection scheme, using a two-lens system to mode-match the interfering beams to achieve background methane detection at a distance of 4.6 m under a 57 dB loss. Our method is also extended to real-world surfaces, such as glass, brushed metal, and a leaf, showing robust background methane sensing with various target materials.

Strong microwave injection into semiconductor laser waveguides has recently gained enormous attention due to the demonstration of novel frequency comb states. The progress in this field demands new theoretical models that are able to accurately describe microwave-optical co-propagation effects along the waveguide. We address this issue by presenting a self-consistent approach based on the Maxwell-density matrix equations. The injected microwave field is included through analytical solutions of the transmission line equations for the specific laser waveguide geometry, which may either be of Fabry–Pérot-type or a ring-like cavity. Based on our model, we show numerical results of real-world devices, which are in excellent agreement with experimental data. This validates the ability of our model to accurately capture the laser dynamics governed by the injection of a strong microwave field.

This Tutorial explores the pivotal role of photonic integrated technologies for future radio-over-fiber systems, covering their operational principles, evolution, and open issues. The core discussion focuses on the advancements toward photonic integration, highlighting recent innovations in hybrid and heterogeneous approaches, the development of fully integrated RoF systems with rare-earth-doped waveguide amplifiers, and the increasing implementation of additional functionalities in these systems, such as frequency upconversion/downconversion and beamforming.