- Mid-infrared spectroscopy with a broadly tunable thin-film lithium niobate optical parametric oscillator OPTICA 2023; 10 (11): 1535-1542
Integrated quantum optical phase sensor in thin film lithium niobate.
2023; 14 (1): 3355
The quantum noise of light, attributed to the random arrival time of photons from a coherent light source, fundamentally limits optical phase sensors. An engineered source of squeezed states suppresses this noise and allows phase detection sensitivity beyond the quantum noise limit (QNL). We need ways to use quantum light within deployable quantum sensors. Here we present a photonic integrated circuit in thin-film lithium niobate that meets these requirements. We use the second-order nonlinearity to produce a squeezed state at the same frequency as the pump light and realize circuit control and sensing with electro-optics. Using 26.2 milliwatts of optical power, we measure (2.7 ± 0.2)% squeezing and apply it to increase the signal-to-noise ratio of phase measurement. We anticipate that photonic systems like this, which operate with low power and integrate all of the needed functionality on a single die, will open new opportunities for quantum optical sensing.
View details for DOI 10.1038/s41467-023-38246-6
View details for PubMedID 37291141
View details for PubMedCentralID 9352777
- Experimental evaluation of digitally verifiable photonic computing for blockchain and cryptocurrency OPTICA 2023; 10 (5): 552-560
Experimentally realized in situ backpropagation for deep learning in photonic neural networks.
Science (New York, N.Y.)
2023; 380 (6643): 398-404
Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using "in situ backpropagation," a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ([Formula: see text]94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.
View details for DOI 10.1126/science.ade8450
View details for PubMedID 37104594
- Power monitoring in a feedforward photonic network using two output detectors NANOPHOTONICS 2023
High-efficiency second harmonic generation of blue light on thin-film lithium niobate.
2022; 47 (11): 2706-2709
The strength of interactions between photons in a chi(2) nonlinear optical waveguide increases at shorter wavelengths. These larger interactions enable coherent spectral translation and light generation at a lower power, over a broader bandwidth, and in a smaller device: all of which open the door to new technologies spanning fields from classical to quantum optics. Stronger interactions may also grant access to new regimes of quantum optics to be explored at the few-photon level. One promising platform that could enable these advances is thin-film lithium niobate (TFLN), due to its broad optical transparency window and possibility for quasi-phase matching and dispersion engineering. In this Letter, we demonstrate second harmonic generation of blue light on an integrated thin-film lithium niobate waveguide and observe a conversion efficiency of eta0=33, 000%/W-cm2, significantly exceeding previous demonstrations.
View details for DOI 10.1364/OL.455046
View details for PubMedID 35648910
- Cascaded optical resonator-based programmable photonic integrated circuits OPTICS EXPRESS 2021; 29 (3): 4645–60