We propose and experimentally demonstrate an optical reservoir computing system in free space, using second-harmonic generation for nonlinear kernel functions and a scattering medium to enhance reservoir nodes interconnection. We test it for one-step and multi-step predication of Mackey–Glass time series with different input-mapping methods on a spatial light modulator. For one-step prediction, we achieve 1.8 × 10⁻³ normalized mean squared error (NMSE). For the multi-step prediction, we explore two different mapping methods: linear-combination and concatenation, achieving 16-step prediction with NMSE as low as 3.5 × 10⁻⁴. Robust and superior for multi-step prediction, our approach and design have potential for parallel data processing tasks such as video prediction, speech translation, and so on.

We introduce a method to measure radio frequency (RF) electric fields (E-fields) using atoms contained in a prism-shaped vapor cell. The method utilizes the concept of electromagnetically induced transparency with Rydberg atoms. The RF E-field induces changes in the index of refraction of the vapor resulting in deflection of the probe laser beam as it passes through the prism-shaped vapor cell. We measured a minimum RF E-field of 8.25 with a sensitivity of . The experimental results agree with a numerical model that includes dephasing effects. We discuss possible improvements to obtain higher sensitivity for RF E-field measurements.

Optical Ising machines promise to solve complex optimization problems with an optical hardware acceleration advantage. Here we study the ground state properties of a nonlinear optical Ising machine realized by spatial light modulator, Fourier optics, and second-harmonic generation in a nonlinear crystal. By tuning the ratio of the light intensities at the fundamental and second-harmonic frequencies, we experimentally observe two distinct ferromagnetic-to-paramagnetic phase transitions: a second-order phase transition where the magnetization changes to zero continuously and a first-order phase transition where the magnetization drops to zero abruptly as the effective temperature increases. Our experimental results are corroborated by a numerical simulation based on the Monte Carlo Metropolis-Hastings algorithm, and the physical mechanism for the distinct phase transitions can be understood with a mean-field theory. Our results showcase the flexibility of the nonlinear optical Ising machine, which may find potential applications in solving combinatorial optimization problems.

We study selective upconversion of optical signals according to their detailed transverse electromagnetic modes and demonstrate its proof of operation in a nonlinear crystal. The mode selectivity is achieved by preparing the pump wave in an optimized spatial profile to drive the upconversion. For signals in the Laguerre–Gaussian modes, we show that a mode can be converted with up to 60 times higher efficiency than an overlapping, but orthogonal, mode. This nonlinear optical approach may find applications in compressive imaging, pattern recognition, quantum communications, and others, where the existing linear optical methods are limited.

With exotic propagation properties, optical Airy beams have been well studied for innovative applications in communications, biomedical imaging, micromachining, and so on. Here we extend those studies to the quantum domain, creating quantum correlated photons in finite-energy Airy transverse modes via spontaneous parametric down conversion and subsequential spatial light modulation. Through two-photon coincidence measurements, we verify their Airy spatial wavefunctions, propagation along a parabolic trajectory, and that the spatial modulation does not introduce any observable degradation of quantum correlation between the photons. These results suggest the feasibility of using spatially structured photons for practically advantageous quantum applications.

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system’s performance and versatility, three benchmark tests are performed. The first is the 10^th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE’s) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73 × 10⁻³ is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.