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The Journal of Physical Chemistry Letters

Neural Ordinary Differential Equations for Forecasting and Accelerating Photon Correlation Spectroscopy

Andrew H. Proppe, Kin Long Kelvin Lee, Weiwei Sun, Chantalle J. Krajewska, Oliver J. Tye, Moungi G. Bawendi

Published Jan 6, 2025• 2 citations
Publisher

Abstract

Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon correlation Fourier spectroscopy (PCFS) is one such technique that measures time-resolved single-emitter line shapes and offers additional spectral information over Hong-Ou-Mandel two-photon interference but requires long experimental acquisition times. Here, we demonstrate a neural ordinary differential equation model, g2NODE, that can forecast a complete and noise-free interferometry experiment from a small subset of noisy correlation functions. We demonstrate this for simulated and experimental data, where g2NODE utilizes 10-20 noisy measured photon correlation functions to create entire denoised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from hours to minutes. Our work presents a new deep learning approach to greatly accelerate the use of photon correlation spectroscopy as an experimental characterization tool for novel quantum emitter materials.

DOI
10.1021/acs.jpclett.4c03234

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Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon correlation Fourier spectroscopy (PCFS) is one such technique that measures time-resolved single-emitter line shapes and offers additional spectral information over Hong-Ou-Mandel two-photon interference but requires long experimental acquisition times. Here, we demonstrate a neural ordinary differential equation model, g2NODE, that can forecast a complete and noise-free interferometry experiment from a small subset of noisy correlation functions. We demonstrate this for simulated and experimental data, where g2NODE utilizes 10-20 noisy measured photon correlation functions to create entire denoised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from hours to minutes. Our work presents a new deep learning approach to greatly accelerate the use of photon correlation spectroscopy as an experimental characterization tool for novel quantum emitter materials.
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.
Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter's surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.
Light emission from solid-state quantum emitters is inherently prone to environmental decoherence, which results in a line broadening and in the deterioration of photon indistinguishability. Here we employ photon correlation Fourier spectroscopy (PCFS) to study the temporal evolution of such a broadening in two prominent systems: GaAs and In(Ga)As quantum dots. Differently from previous experiments, the emitters are driven with short laser pulses as required for the generation of high-purity single photons, the time scales we probe range from a few nanoseconds to milliseconds and, simultaneously, the spectral resolution we achieve can be as small as ∼ 2µeV. We find pronounced differences in the temporal evolution of different optical transition lines, which we attribute to differences in their homogeneous linewidth and sensitivity to charge noise. We analyze the effect of irradiation with additional white light, which reduces blinking at the cost of enhanced charge noise. Due to its robustness against experimental imperfections and its high temporal resolution and bandwidth, PCFS outperforms established spectroscopy techniques, such as Michelson interferometry. We discuss its practical implementation and the possibility to use it to estimate the indistinguishability of consecutively emitted single photons for applications in quantum communication and photonic-based quantum information processing.
Second-order photon correlation measurements [${g}^{(2)}(\ensuremath{\tau})$ functions] are widely used to classify single-photon emission purity in quantum emitters or to measure the multiexciton quantum yield of emitters that can simultaneously host multiple excitations -- such as quantum dots -- by evaluating the value of ${g}^{(2)}(\ensuremath{\tau}=0)$. Accumulating enough photons to accurately calculate this value is time consuming and could be accelerated by fitting of few-shot photon correlations. Here, we develop an uncertainty-aware, deep adversarial autoencoder ensemble (AAE) that reconstructs noise-free ${g}^{(2)}(\ensuremath{\tau})$ functions from noise-dominated, few-shot inputs. The model is trained with simulated ${g}^{(2)}(\ensuremath{\tau})$ functions that are facilely generated by Poisson sampling time bins. The AAE reconstructions are performed orders-of-magnitude faster, with reconstruction errors and estimates of ${g}^{(2)}(\ensuremath{\tau}=0)$ that are lower in variance and similar in accuracy compared to Maximum likelihood estimation and Levenberg-Marquardt least-squares fitting approaches, for simulated and experimentally measured few-shot ${g}^{(2)}(\ensuremath{\tau})$ functions (\ensuremath{\sim}100 two-photon events) of InP/ZnS/ZnSe and CdS/CdSe/CdS quantum dots. The deep-ensemble model comprises eight individual autoencoders, allowing for probabilistic reconstructions of noise-free ${g}^{(2)}(\ensuremath{\tau})$ functions, and we show that the predicted variance scales inversely with number of shots, with comparable uncertainties to computationally intensive Markov chain Monte Carlo sampling. This work demonstrates the advantage of machine learning models to perform uncertainty-aware, fast, and accurate reconstructions of simple Poisson-distributed photon correlation functions, allowing for on-the-fly reconstructions and accelerated materials characterization of solid-state quantum emitters.
The excited state population of single solid-state emitters is subjected to energy fluctuations around the equilibrium driven by the bath and relaxation through the emission of phonons or photons. Simultaneous measurement of the associated spectral dynamics requires a technique with a high spectral and temporal resolution with an additionally high temporal dynamic range. We propose a pulsed excitation-laser analog of photon-correlation Fourier spectroscopy (PCFS), which extracts the linewidth and spectral diffusion dynamics along the emission lifetime trajectory of the emitter, effectively discriminating spectral dynamics from relaxation and bath fluctuations. This lifetime-resolved PCFS correlates photon-pairs at the output arm of a Michelson interferometer in both their time-delay between laser-excitation and photon-detection T and the time-delay between two photons τ. We propose the utility of the technique for systems with changing relative contributions to the emission from multiple states, for example, quantum emitters exhibiting phonon-mediated exchange between different fine-structure states.
Photon emission correlation spectroscopy is an indispensable tool for the study of atoms, molecules, and, more recently, solid-state quantum defects. In solid-state systems, its most common use is as an indicator of single-photon emission, a key property for quantum technology. Beyond an emitter's single-photon purity, however, photon correlation measurements can provide a wealth of information that can reveal details about its electronic structure and optical dynamics that are hidden by other spectroscopy techniques. This tutorial presents a standardized framework for using photon emission correlation spectroscopy to study quantum emitters, including discussion of theoretical background, considerations for data acquisition and statistical analysis, and interpretation. We highlight important nuances and best practices regarding the commonly-used g(2)(tau=0)<0.5g^{(2)}(\\tau=0)<0.5g(2)(tau=0)<0.5 test for single-photon emission. Finally, we illustrate how this experimental technique can be paired with optical dynamics simulations to formulate an electronic model for unknown quantum emitters, enabling the design of quantum control protocols and assessment of their suitability for quantum information science applications.