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Chantalle J. Krajewska

Publications and source records attributed to Chantalle J. Krajewska.

2 recordsLinked to original sources

Exciton Coherence in CsPbBr3 Nanocrystals is Bounded by Phonon-Mediated Bright-Triplet Relaxation

Scalable sources of indistinguishable single photons or entangled photon pairs are fundamental to many quantum photonic technologies. Colloidal lead halide perovskite nanocrystals are promising such sources, but their exciton coherent properties are not fully understood. We show that in single CsPbBr3 nanocrystals at 4 K, acoustic phonon-mediated exciton fine structure relaxation (EFSR) drives leakage of population between the bright exciton triplet states, competing with the radiative lifetime. The leakage pathway reaches 0.64 $\pm$ 0.04 of the radiative rate, bounding state coherence to 0.81 times the transform limit at 4 K. This pathway is intrinsic to the nanocrystal, not its environment, making it a tractable target for materials and device design. We identify fine structure and acoustic phonon engineering as effective intrinsic routes to higher coherence in perovskite quantum light sources.

cond-mat.mtrl-sci↗

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↗