Entangled two-photon absorption (eTPA) has been recognized as a potentially powerful tool for the implementation of ultra-sensitive spectroscopy. Unfortunately, there exists a general agreement in the quantum optics community that experimental eTPA signals, particularly those obtained from molecular solutions, are extremely weak. Consequently, obtaining spectroscopic information about an arbitrary sample via conventional methods rapidly becomes an unrealistic endeavor. To address this problem, the authors introduce an experimental scheme that reduces the amount of data needed to identify and classify unknown samples via their electronic structure. Their proposed method makes use of machine learning to extract information about the number of intermediate levels that participate in the two-photon excitation of the absorbing medium. This is achieved by training artificial neural networks (ANNs) with various eTPA signals where the delay between the absorbed photons is externally controlled. Inspired by multiple experimental studies of eTPA, the authors consider model systems comprising one to four intermediate levels, whose energies are randomly chosen from four different intermediate-level bandgaps, namely, Δλ=10, 20, 30, and 40 nm. Within these bandgaps, and with the goal of testing the efficiency of their artificial intelligence algorithms, the authors make use of three different wavelength spacing 1, 0.5, and 0.1 nm. The authors find that for a proper entanglement time between the absorbed photons, classification average efficiencies exceed 99% for all configurations. Their results demonstrate the potential of ANNs for facilitating the experimental implementation of eTPA spectroscopy.
Entangled two-photon absorption (eTPA) has been recognized as a potentially powerful tool for the implementation of ultra-sensitive spectroscopy. Unfortunately, there exists a general agreement in the quantum optics community that experimental eTPA signals, particularly those obtained from molecular solutions, are extremely weak. Consequently, obtaining spectroscopic information about an arbitrary sample via conventional methods rapidly becomes an unrealistic endeavor. To address this problem, the authors introduce an experimental scheme that reduces the amount of data needed to identify and classify unknown samples via their electronic structure. Their proposed method makes use of machine learning to extract information about the number of intermediate levels that participate in the two-photon excitation of the absorbing medium. This is achieved by training artificial neural networks (ANNs) with various eTPA signals where the delay between the absorbed photons is externally controlled. Inspired by multiple experimental studies of eTPA, the authors consider model systems comprising one to four intermediate levels, whose energies are randomly chosen from four different intermediate-level bandgaps, namely, Δλ=10, 20, 30, and 40 nm. Within these bandgaps, and with the goal of testing the efficiency of their artificial intelligence algorithms, the authors make use of three different wavelength spacing 1, 0.5, and 0.1 nm. The authors find that for a proper entanglement time between the absorbed photons, classification average efficiencies exceed 99% for all configurations. Their results demonstrate the potential of ANNs for facilitating the experimental implementation of eTPA spectroscopy.
Entangled two-photon absorption spectroscopy (TPA) has been widely recognized as a powerful tool for revealing relevant information about the structure of complex molecular systems. However, to date, the experimental implementation of this technique has remained elusive, mainly because of two major difficulties: first, the need to perform multiple experiments with two-photon states bearing different temporal correlations, which translates into the necessity to have at the experimenter's disposal tens, if not hundreds, of sources of entangled photons; second, the need to have a priori knowledge of the absorbing medium's lowest-lying intermediate energy level. In this work, we put forward a simple experimental scheme that successfully overcomes these two limitations. By making use of a temperature-controlled entangled-photon source, which allows the tuning of the central frequencies of the absorbed photons, we show that the TPA signal, measured as a function of the temperature of the nonlinear crystal that generates the paired photons, and a controllable delay between them, carries all information about the electronic level structure of the absorbing medium, which can be revealed by a simple Fourier transformation.
In entangled two-photon absorption (eTPA) spectroscopy, information about the energy-level structure of an arbitrary sample is retrieved by Fourier transforming sets of measured two-photon absorption probabilities of entangled photon pairs where the degree of entanglement and the delay time between the photons are varied. This works well for simple systems but quickly becomes rather difficult when many intermediate states are involved. We propose and discuss an extension of eTPA spectroscopy that solves this problem by means of repeated measurements at different pump wavelengths, thus reducing the experimental complexity of eTPA spectroscopy by removing the need to control the correlations of the entangled photons. Specifically, we demonstrate that our extension works well for a variety of realistic experimental setups.
Multiphoton absorption of entangled photons offers ways for obtaining unique information about chemical and biological processes. Measurements with entangled photons may enable sensing biological signatures with high selectivity and at very low light levels to protect against photodamage. In this paper, we present a theoretical and experimental study of the excitation wavelength dependence of the entangled two-photon absorption (ETPA) process in a molecular system, which provides insights into how entanglement affects molecular spectra. We demonstrate that the ETPA excitation spectrum can be different from that of classical TPA as well as that for one-photon resonant absorption (OPA) with photons of doubled frequency. These results are modeled by assuming the ETPA cross-section is governed by a two-photon excited state radiative linewidth rather than by electron-phonon interactions, and this leads to excitation spectra that match the observed results. Further, we find that the two-photon-allowed states with highest TPA and ETPA intensities have high electronic entanglements, with ETPA especially favoring states with the longest radiative lifetimes. These results provide concepts for the development of quantum light-based spectroscopy and microscopy that will lead to much higher efficiency of ETPA sensors and low-intensity detection schemes.
Recent investigations suggest that the use of non-classical states of light, such as entangled photon pairs, may open new and exciting avenues in experimental two-photon absorption spectroscopy. Despite several experimental studies of entangled two-photon absorption (eTPA), there is still a heated debate on whether eTPA has truly been observed. This interesting debate has arisen, mainly because it has been recently argued that single-photon-loss mechanisms, such as scattering or hot-band absorption may mimic the expected entangled-photon linear absorption behavior. In this work, we focus on transmission measurements of eTPA, and explore three different two-photon quantum interferometers in the context of assessing eTPA. We demonstrate that the so-called N00N-state configuration is the only one amongst those considered insensitive to linear (single-photon) losses. Remarkably, our results show that N00N states may become a potentially powerful tool for quantum spectroscopy, and place them as a strong candidate for the certification of eTPA in an arbitrary sample.
By controlling quantum interactions of molecules and light, new technology in quantum information science promises to outperform classical-based technology in both function and efficiency. In particular, chemists stand to gain much advantage from quantum sensing techniques, allowing them to measure new spectroscopic signals that classical techniques cannot do, control photochemical reactions in new ways, and even measure nonlinear interactions with a simple push of a button. Much of the current work in using entangled photons for spectroscopy has focused on the unique physics of the photons. There is still much to be learned about how the molecule’s structure influences quantum spectroscopic signals and leaves its signature. In this dissertation, the unique ways that organic chromophores interact with entangled photons from spontaneous parametric down-conversion (SPDC) has been analyzed in detail. In Chapter 3, I calculate entangled two-photon absorption (ETPA) cross-sections for a few diatomic molecules to show how the varying dipole properties of the molecules affect the ETPA cross-section oscillation vs the entanglement time, Te. In Chapter 4, new theory shows that the excited state linewidth from ETPA excitation is extremely narrow compared to classical TPA excitation. I show that the reason for the ETPA linewidth being so narrow is due to the large arrival time uncertainty of the entangled photon pair, which makes the first photons’ absorption time in the molecule largely uncertain and effectively narrows the excited state linewidth. In Chapter 5, I used Type-II SPDC to prove, for the first time, that two halogenated anesthetic ethers, sevoflurane and isoflurane, have the ability to target and interact with quantum particles by showing that they interact with 800 nm entangled photons but not 800 nm classical photons. In Chapter 6, I build an ETPA experimental setup that uses CW laser pumping of Type-I SPDC, which is shown to significantly improve the accuracy, signal-to-noise ratio (SNR), and limit of detection of ETPA while also making the experiment more compact and cost-effective. I also show how to quantify the degree of frequency entanglement using the Schmidt decomposition of the joint frequency spectrum and propose how the Schmidt modes can be tuned to control photochemical reactions. Contrary to prior belief in the literature, I show experimentally in Chapter 7 that ETPA with Type-I SPDC increases linearly with increasing Te, allowing one to increase the ETPA cross-section by an order of magnitude by increasing Te from a few fs to ~10 ps. This enhancement is due to the entanglement area, Ae, decreasing as the frequency bandwidth decreases, which is how Te is increased. This spatial-spectral coupling has previously been overlooked in the context of ETPA, causing previous theoretical models to underestimate the ETPA cross-section at ps Te by 3-4 orders of magnitude. Using ps Te Type-I SPDC, ps-scale excited state dynamics can be studied using ETPA, and photochemical reactions with intermediate steps, such as isomerizations or solvent reorganization, can be controlled. The experimental techniques and theoretical work here has provided a much needed improvement to make ETPA a more quantitatively robust analytical technique that is useful and accessible to the researchers who can benefit most from its applications: chemists, biologists, and anyone who studies the quantum principles underlying all aspects of chemistry.
Quantum state classification and entanglement quantification are of significant importance in the fundamental research of quantum information science and various quantum applications. Traditional methods, such as quantum state tomography, face exponential measurement demands with increasing numbers of qubits, necessitating more efficient approaches. Recent work has shown promise in using artificial neural networks (ANNs) for quantum state analysis. However, existing ANNs may falter when confronted with states affected by dephasing noise, especially with limited data and computational resources. In this study, we employ a machine-learning approach to investigate the disentanglement process in two-qubit systems in the presence of dephasing noise. Our findings highlight the limitations of general state-trained ANNs in classifying states under dephasing noise. Specialized ANN algorithms, tailored for classifying states and quantifying entanglement in such noisy environments, demonstrate excellent performance using only a subset of tomographic features.