arXiv subjects

Ryan T. Glasser

Publications and source records attributed to Ryan T. Glasser.

At least 37 records · Page 2Linked to original sources

arXiv2020

Spatial Mode Correction of Single Photons using Machine Learning

Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at the single-photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, we exploit the self-learning and self-evolving features of artificial neural networks to correct the complex spatial profile of distorted Laguerre-Gaussian modes at the single-photon level. Furthermore, we demonstrate the possibility of boosting the performance of an optical communication protocol through the spatial mode correction of single photons using machine learning. Our results have important implications for real-time turbulence correction of structured photons and single-photon images.

quant-ph↗

arXiv2020

Machine learning assisted quantum state estimation

We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.

quant-ph↗

arXiv2019

Coherent Optical Communications Enhanced by Machine Intelligence

Uncertainty in discriminating between different received coherent signals is integral to the operation of many free-space optical communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communications scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we program the neural network system at the transmitter such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is shared with the network state of the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. This communications design is straightforward to build, implement, and scale. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.

quant-ph↗

arXiv2019

Generative Machine Learning for Robust Free-Space Communication

Realistic free-space optical communications systems suffer from turbulent propagation of light through the atmosphere and detector noise at the receiver, which can significantly degrade the optical mode quality of the received state, increase cross-talk between modes, and correspondingly increase the symbol error ratio (SER) of the system. In order to overcome these obstacles, we develop a state-of-the-art generative machine learning (GML) and convolutional neural network (CNN) system in combination, and demonstrate its efficacy in a free-space optical (FSO) communications setting. The system corrects for the distortion effects due to turbulence and reduces detector noise, resulting in significantly lowered SERs and cross-talk at the output of the receiver, while requiring no feedback. This scheme is straightforward to scale, and may provide a concrete and cost effective technique to establishing long range classical and quantum communication links in the near future.

eess.SP↗

arXiv2019

Dispersion Characterization and Pulse Prediction with Machine Learning

In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses which propagate through a nonlinear dispersive medium, which may be applied to predicting optimal pulse shapes for a desired output. The setup requires only a single pulse for the probe, providing considerable simplification of the current method of dispersion characterization that requires frequency scanning across the entirety of the gain and absorption features. We show that the trained networks are able to predict pulse profiles as well as dispersive features that are nearly identical to their experimental counterparts. We anticipate that the use of machine learning in conjunction with optical communication and sensing methods, both classical and quantum, can provide signal enhancement and experimental simplifications even in the face of highly complex, layered nonlinear light-matter interactions.

physics.optics↗

arXiv2018

Optimal mode configuration for multiple phase-matched four-wave mixing processes

We demonstrate an unseeded, multimode four-wave mixing process in hot $^{85}$Rb vapor, using two pump beams of the same frequency that cross at a small angle. This results in the simultaneous fulfillment of multiple phase-matching conditions that reinforce one another to produce four intensity-stabilized bright output modes at two different frequencies. Each generated photon is directly correlated to exactly two others, resulting in the preferred four-mode output, in contrast to other multimode four-wave mixing experiments. This provides significant insight into the optimal configuration of the output multimode squeezed and entangled states generated in such four-wave mixing systems. We examine the power, temperature and frequency dependence of this new output and compare to the conical four-wave mixing emission from a single pump beam. The generated beams are spatially separated, allowing a natural distribution for potential use in quantum communication and secret-sharing protocols.

quant-ph↗

arXiv2018

Turbulence correction with artificial neural networks

We design an optical feedback network making use of machine learning techniques and demonstrate via simulations its ability to correct for the effects of turbulent propagation on optical modes. This artificial neural network scheme only relies on measuring the intensity profile of the distorted modes, making the approach simple and robust. The network results in the generation of various mode profiles at the transmitter that, after propagation through turbulence, closely resemble the desired target mode. The corrected optical mode profiles at the receiver are found to be nearly identical to the desired profiles, with near-zero mean square error indices. We are hopeful that the present results combining the fields of machine learning and optical communications will greatly enhance the robustness of free-space optical links.

eess.SP↗

arXiv2018

On the use of deep neural networks in optical communications

Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep neural networks to classify numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.

physics.app-ph↗

arXiv2018

Faster light with competing absorption and gain

We experimentally investigate the propagation of optical pulses through a fast-light medium with competing absorption and gain. The combination of strong absorption and optical amplification in a potassium-based four-wave mixing process results in pulse peak advancements up to $88\%$ of the input pulse width, more than $35 \times$ that which is achievable without competing absorption. We show that the enhancement occurs even when the total gain of the four-wave mixer is unity, thereby rendering the medium transparent. By varying the pulse width, we observe a transition between fast and slow light, and show that fast light is optimized for large pulse widths.

physics.optics↗

arXiv2018

Multi-mode four-wave mixing with a spatially-structured pump

We demonstrate a new four-wave mixing (4WM) geometry based on structured light. By utilizing near-field diffraction through a narrow slit, the pump beam is asymmetrically structured to modify the phase matching condition, generating multi-mode output in both the spatial and frequency domains. We show that the frequency parameter enables selection of various spatial-mode outputs, including a twin-beam geometry which preserves relative intensity squeezing shared between the two beams. The results suggest that the engineering of atomic states via structured light may provide a pathway to a diverse set of quantum resources based on multi-mode squeezed light.

quant-ph↗

arXiv2017

Room-Temperature Photon-Number-Resolved Detection Using A Two-Mode Squeezer

We study the average coincidence-count signal at the output of a two-mode squeezing device with $|N\rangle\otimes|α\rangle$ as the two input modes. We show that the input photon-number can be resolved from the average coincidence counts. In particular, we show jumps in the average coincidence-count signal as a function of input photon-number $N$. Therefore, we propose that such a device may be deployed as photon-number-resolving detector at room temperature with high efficiency.

quant-ph↗

arXiv2017

Characterizing micro-macro transitions with an atomic-vapor-based linear optical amplifier

Fundamentally, the dynamics of micro-macro transitions is instrumental to understanding the process of quantum-to-classical transitions; technologically, it can also facilitate the detection of the microscopic signals in quantum experiments via convenient detectors. Here, we demonstrate a scheme to characterize micro-macro transitions based on a four-wave mixing linear optical amplification process in a hot rubidium vapor. The linear optical amplifier provides a large optical gain of $10^7$ for injected single-photon-level pulses, enabling photon-number-resolving detection by average via non-single-photon counting detectors with a large dynamic range. The scheme exhibits strong dispersion which is sensitive to the input's change at the single-photon level, resulting in the group-velocity delay time scaling with $1/\sqrt{N}$, where $N$ is the average input photon number. The output probe and conjugate modes have different coefficients of this $1/\sqrt{N}$ scaling, indicating the coefficient can serve as an efficient parameter to characterize the specified micro-macro transitions. The demonstrated results are generally applicable for quantum detection and optical signal processing in light-atom interfaces. Furthermore, the present system is suitable for the study of relevant time-resolved dynamics of the quantum-to-classical transitions.

quant-ph↗

arXiv2017

Squeezed-twin-beam generation in strongly absorbing media

In this Letter we experimentally demonstrate the generation of squeezed, bright twin beams which arise due to competing gain and absorption, in a medium that is overall transparent. To accomplish this, we make use of a non-degenerate four-wave mixing process in warm potassium vapor, such that one of the twin beams experiences strong on-resonant absorption. At room temperature and above, due to Doppler broadening and smaller frequency detunings compared to other schemes, the ground state hyperfine splittings used in the present double-$Λ$ setup are completely overlapped. We show that despite the resulting significant asymmetric absorption of the twin beams, quantum correlations may still be generated. Our results also demonstrate that the simplified model of gain, followed by loss, is insufficient to describe the amount of quantum correlation resulting from the process.

quant-ph↗

arXiv2017

Causality and information transfer in simultaneously slow- and fast-light media

We demonstrate the simultaneous propagation of slow- and fast-light optical pulses in a four-wave mixing scheme using warm potassium vapor. We show that when the system is tuned such that the input probe pulses exhibit slow-light group velocities and the generated pulses propagate with negative group velocities, the information velocity in the medium is nonetheless constrained to propagate at, or less than, c. These results demonstrate that the transfer and copying of information on optical pulses to those with negative group velocities obeys information causality, in a manner that is reminiscent of a classical version of the no-cloning theorem. Additionally, these results support the fundamental concept that points of non-analyticity on optical pulses correspond to carriers of new information.

physics.optics↗

arXiv2017

Atomic vapor as a source of tunable, non-Gaussian self-reconstructing optical modes

In this manuscript, we demonstrate the ability of nonlinear light-atom interactions to produce tunably non-Gaussian, partially self-healing optical modes. Gaussian spatial-mode light tuned near to the atomic resonances in hot rubidium vapor is shown to result in non-Gaussian output mode structures that may be controlled by varying either the input beam power or the temperature of the atomic vapor. We show that the output modes exhibit a degree of self-reconstruction after encountering an obstruction in the beam path. The resultant modes are similar to truncated Bessel-Gauss modes that exhibit the ability to self-reconstruct earlier upon propagation than Gaussian modes. The ability to generate tunable, self-reconstructing beams has potential applications to a variety of imaging and communication scenarios.

physics.optics↗

arXiv2016

All-optical mode conversion via spatially-multimode four-wave mixing

We experimentally demonstrate the conversion of a Gaussian beam to an approximate Bessel-Gauss mode by making use of a non-collinear four-wave mixing process in hot atomic vapor. The presence of a strong, spatially non-Gaussian pump both converts the probe beam into a non-Gaussian mode, and generates a conjugate beam that is in a similar non-Gaussian mode. The resulting probe and conjugate modes are compared to the output of a Gaussian beam incident on an annular aperture that is then spatially filtered according to the phase-matching conditions imposed by the four-wave mixing process. We find that the resulting experimental data agrees well with both numerical simulations, as well as analytical formulae describing the effects of annular apertures on Gaussian modes. These results show that spatially-multimode gain platforms may be used as a new method of mode conversion.

quant-ph↗

arXiv2014

Quantum mutual information of an entangled state propagating through a fast-light medium

Although it is widely accepted that classical information cannot travel faster than the speed of light in vacuum, the behavior of quantum correlations and quantum information propagating through actively-pumped fast-light media has not been studied in detail. To investigate this behavior, we send one half of an entangled state of light through a gain-assisted fast-light medium and detect the remaining quantum correlations. We show that the quantum correlations can be advanced by a small fraction of the correlation time while the entanglement is preserved even in the presence of noise added by phase-insensitive gain. Additionally, although we observe an advance of the peak of the quantum mutual information between the modes, we find that the degradation of the mutual information due to the added noise appears to prevent an advancement of the leading edge. In contrast, we show that both the leading and trailing edges of the mutual information in a slow-light system can be significantly delayed.

quant-ph↗

arXiv2013

Advanced Quantum Noise

We use the quantum correlations of twin-beams of light to probe the added noise when one of the beams propagates through a medium with anomalous dispersion. The experiment is based on two successive four-wave mixing processes in rubidium vapor, which allow for the generation of bright two-mode-squeezed twin-beams followed by a controlled advancement while maintaining the shared quantum-correlations between the beams. The demonstrated effect allows the study of irreversible decoherence in a medium exhibiting anomalous dispersion, and for the first time shows the advancement of a bright nonclassical state of light. The advancement and corresponding degradation of the quantum correlations are found to be operating near the fundamental quantum limit imposed by using a phase-insensitive amplifier.

quant-ph↗