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Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7±0.2dB of directly detected nanophotonic quantum squeezing using foundry-fabricated silicon nitride (Si₃N₄) microrings with an inferred squeezing level of 10.2 dB on-chip. The squeezing level is robust across multiple devices and pump detunings, and is consistent with the overcoupling degree without noticeable degradation from excess classical noise. We also offer insights to mitigate thermally induced excess noise, which typically degrades squeezing, by using small-radius rings with a larger free spectral range (450 GHz) and consequently lower parametric oscillation thresholds. Our results demonstrate that Si₃N₄is a viable platform for strong quantum noise reduction in a CMOS-compatible, scalable architecture. 

We demonstrate spectral phase transitions in dual-pumped Si₃N₄nanophotonic Kerr optical parametric oscillators with anomalous dispersion. Through pump-power modulation, we observe real-time switching between a near-degenerate signal (0-FSR separation) and non-degenerate signals (4-FSR separation). 

Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however, these machines typically suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems. 

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using backpropagation - where the gradient is analytical, not numerical.