arXiv subjects

Andrew H. Proppe

Publications and source records attributed to Andrew H. Proppe.

6 recordsLinked to original sources

arXiv2026

Simultaneous amplitude and phase spectroscopy using two-photon interference

Quantum spectroscopy seeks to probe chemical systems using nonclassical light, which has properties that are qualitatively and quantitatively different than conventional light sources. One promising technique uses intensity-correlated twin beams of light to reduce the noise sources inherent to absorption spectroscopy. However, measurements of the phase shift imparted by the chemical sample, which provides complementary information to the absorption, continue to be a challenge. Here, we propose and demonstrate a scheme using entangled photon pairs that can simultaneously measure both the absorption and phase shift of a sample with extremely low optical intensities and with relatively fast few-minute acquisition times. This method combines the previous use of intensity correlations with a broadband quantum interferometer utilizing two-photon interference to measure the complete linear optical response of the sample. Our work shows that precise measurements of absorption can also be made phase-sensitive using suitable choices of probe beam and detection scheme. This enables a new class of quantum spectroscopy schemes which measure absorption and phase with a single probe. Our technique is relevant to the characterization of a wide array of chemical and biological samples, such as quantum dots and organic fluorophores, and may be useful for spectroscopic measurements that are otherwise constrained in intensity.

quant-ph↗

arXiv2025

Ultrafast switching of telecom photon-number states

A crucial component of photonic quantum information processing platforms is the ability to modulate, route, convert, and switch quantum states of light noiselessly with low insertion loss. For instance, a high-speed, low-loss optical switch is crucial for scaling quantum photonic systems that rely on measurement-based feed-forward approaches. Such a device will also ideally be capable of operating on photon-number states, which can act as non-Gaussian resources. Here, we demonstrate ultrafast all-optical switching of heralded photon-number states, of up to 6 photons, using the optical Kerr effect in a single-mode fiber. A local birefringence is created by a high-intensity pump pulse at a center wavelength of 1030 nm which overlaps temporally with the 1550 nm photons in the fiber. A switching efficiency of $>$99 % is reached with a resolution of 2.3 ps, an insertion loss of $2.27\pm0.08$ dB, and a signal-to-noise ratio of 32,000.

quant-ph↗

arXiv2025

Imaging at the quantum limit with convolutional neural networks

Deep neural networks have been shown to achieve exceptional performance for computer vision tasks like image recognition, segmentation, and reconstruction or denoising. Here, we evaluate the ultimate performance limits of deep convolutional neural network models for image reconstruction, by comparing them against the standard quantum limit set by shot-noise and the Heisenberg limit on precision. We train U-Net models on images of natural objects illuminated with coherent states of light, and find that the average mean-squared error of the reconstructions can surpass the standard quantum limit, and in some cases reaches the Heisenberg limit. Further, we train models on well-parameterized images for which we can calculate the quantum Cramér-Rao bound to determine the minimum possible measurable variance of an estimated parameter for a given probe state. We find the mean-squared error of the model predictions reaches these bounds calculated for the parameters, across a variety of parameterized images. These results suggest that deep convolutional neural networks can learn to become the optimal estimators allowed by the laws of physics, performing parameter estimation and image reconstruction at the ultimate possible limits of precision for the case of classical illumination of the object.

cs.LG↗

arXiv2024

Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations

Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments, such as Photon correlation Fourier spectroscopy (PCFS) which measures time-resolved single emitter lineshapes. Here, we demonstrate a latent neural ordinary differential equation model that can forecast a complete and noise-free PCFS experiment from a small subset of noisy correlation functions. By encoding measured photon correlations into an initial value problem, the NODE can be propagated to an arbitrary number of interferometer delay times. We demonstrate this with 10 noisy photon correlation functions that are used to extrapolate an entire de-noised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from $\sim$3 hours to 10 minutes. Our work presents a new approach to greatly accelerate the experimental characterization of novel quantum emitter materials using deep learning.

quant-ph↗

arXiv2024

3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging

In recent years, neural networks have been used to solve phase retrieval problems in imaging with superior accuracy and speed than traditional techniques, especially in the presence of noise. However, in the context of interferometric imaging, phase noise has been largely unaddressed by existing neural network architectures. Such noise arises naturally in an interferometer due to mechanical instabilities or atmospheric turbulence, limiting measurement acquisition times and posing a challenge in scenarios with limited light intensity, such as remote sensing. Here, we introduce a 3D-2D Phase Retrieval U-Net (PRUNe) that takes noisy and randomly phase-shifted interferograms as inputs, and outputs a single 2D phase image. A 3D downsampling convolutional encoder captures correlations within and between frames to produce a 2D latent space, which is upsampled by a 2D decoder into a phase image. We test our model against a state-of-the-art singular value decomposition algorithm and find PRUNe reconstructions consistently show more accurate and smooth reconstructions, with a x2.5 - 4 lower mean squared error at multiple signal-to-noise ratios for interferograms with low (< 1 photon/pixel) and high (~100 photons/pixel) signal intensity. Our model presents a faster and more accurate approach to perform phase retrieval in extremely low light intensity interferometry in presence of phase noise, and will find application in other multi-frame noisy imaging techniques.

physics.optics↗

arXiv2022

Uncovering Temperature-Dependent Exciton-Polariton Relaxation Mechanisms in Perovskites

State-of-the-art hybrid perovskites have demonstrated excellent functionality in photovoltaics and light-emitting applications, and have emerged as a promising candidate for exciton-polariton (polariton) optoelectronics. In the strong coupling regime, polariton formation and Bose-Einstein condensation (BEC) have been demonstrated at room-temperature in several perovskite formulations. Thermodynamically, low-threshold BEC requires efficient scattering to $k_{||}$ = 0, and many applications demand precise control of polariton interactions. Thus far, the primary mechanisms by which polaritons relax in perovskites remains unclear. In this work, we perform temperature-dependent measurements of polaritons in low-dimensional hybrid perovskites with high light-matter coupling strengths ($\hbar Ω_{Rabi}$ = 260$\pm$5 meV). By embedding the perovskite active layer in a wedged cavity, we are able to tune the Hopfield coefficients and decouple the primary polariton relaxation mechanisms in this material for the first time. We observe the thermal activation of a bottleneck regime, and reveal that this effect can be overcome by harnessing intrinsic scattering mechanisms arising from the interplay between the different excitonic species, such as biexciton-assisted polariton relaxation pathways, and isoenergetic intracavity pumping. We demonstrate the dependence of the bottleneck suppression on cavity detuning, and are able to achieve efficient relaxation to $k_{||}$ = 0 even at cryogenic temperatures. This new understanding contributes to the design of ultra-low-threshold BEC and condensate control by engineering polariton dispersions resonant with efficient relaxation pathways, leveraging intrinsic material scattering mechanisms for next-generation polariton optoelectronics.

cond-mat.mes-hall↗