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Physical Review Applied

Experimental demonstration of deep-learning-enabled adaptive optics

Hao-Bin Fu, Zu-Yang Wan, Yu-Huai Li, Bo Li, Zhen Rong, Gao-Qiang Wang, Juan Yin, Ji-Gang Ren, Wei-Yue Liu, Sheng‐Kai Liao, Yuan Wen Cao, Cheng-Zhi Peng

Published Sep 19, 2024• 9 citations
Publisher

Abstract

Satellite-based quantum communication is a promising approach for establishing global-scale quantum networks. In free-space quantum channels, single-mode-fiber coupling plays a crucial role in increasing the signal-to-noise ratio of daylight quantum key distribution (QKD) and ensuring compatibility with standard fiber-based QKD protocols. However, consistently achieving high efficiency and stable single-mode-fiber coupling under strong atmospheric turbulence remains an ongoing experimental challenge. In this study, we experimentally demonstrate an adaptive method based on convolutional neural networks capable of directly estimating phase information from a single defocused image. We developed a convolutional neural network to establish the relationship between intensity distribution and phase information of turbulent distortions. We demonstrate the real-time performance of our deep-learning adaptive method in increasing single-mode-fiber coupling efficiency across various turbulence scales and quantify turbulence frequencies. Notably, the method proved highly effective in strong-turbulence scenarios, with frequencies reaching up to 200 Hz, leading to a significant increase in single-mode-fiber coupling efficiency. We demonstrate the corrective capability of our adaptive method for strong turbulence, enabled by the generalization of the convolutional neural network. Our results offer an efficient solution for daytime free-space QKD applications.

DOI
10.1103/physrevapplied.22.034047

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Satellite-based quantum communication is a promising approach for establishing global-scale quantum networks. In free-space quantum channels, single-mode-fiber coupling plays a crucial role in increasing the signal-to-noise ratio of daylight quantum key distribution (QKD) and ensuring compatibility with standard fiber-based QKD protocols. However, consistently achieving high efficiency and stable single-mode-fiber coupling under strong atmospheric turbulence remains an ongoing experimental challenge. In this study, we experimentally demonstrate an adaptive method based on convolutional neural networks capable of directly estimating phase information from a single defocused image. We developed a convolutional neural network to establish the relationship between intensity distribution and phase information of turbulent distortions. We demonstrate the real-time performance of our deep-learning adaptive method in increasing single-mode-fiber coupling efficiency across various turbulence scales and quantify turbulence frequencies. Notably, the method proved highly effective in strong-turbulence scenarios, with frequencies reaching up to 200 Hz, leading to a significant increase in single-mode-fiber coupling efficiency. We demonstrate the corrective capability of our adaptive method for strong turbulence, enabled by the generalization of the convolutional neural network. Our results offer an efficient solution for daytime free-space QKD applications.
Satellite-based quantum communication is a promising approach for realizing global-scale quantum networks. For free-space quantum channel, single-mode fiber coupling is particularly important for improving the signal-to-noise ratio of daylight quantum key distribution (QKD) and compatibility with standard fiber-based QKD. However, achieving a highly efficient and stable single-mode coupling efficiency under strong atmospheric turbulence remains experimentally challenging. Here, we develop a single-mode receiver with an adaptive optics (AO) system based on a modal version of the stochastic parallel gradient descent (M-SPGD) algorithm and test its performance over an 8 km urban terrestrial free-space channel. Under strong atmospheric turbulence, the M-SPGD AO system obtains an improvement of about 3.7 dB in the single-mode fiber coupling efficiency and a significant suppression of fluctuation, which can find its applications in free-space long-range quantum communications.
Abstract Satellite-based quantum key distribution (QKD) faces the challenge of wavefront aberration in free-space links. The aberration causes a mismatch between the mode field of the received beam and the single mode fiber (SMF), thereby reducing the coupling efficiency of the SMF and affecting the system’s performance. Here, adaptive optics (AO) based on diffractive deep neural network (D2NN) is applied to satellite-based QKD system to achieve rapid and effective wavefront correction. Simulation results show that the coupling efficiency has been improved under different turbulence intensities, demonstrating and large dynamic range of D2NN. Especially under strong turbulence, coupling efficiency has led to an order-of-magnitude increase in the average key rate of satellite-based QKD. Compared to AO based on adaptive stochastic parallel gradient descent (ASPGD) algorithm, D2NN AO has the advantages of light-speed real-time correction and stability. Furthermore, using reflective D2NN compensates for low transmittance of the network layers. D2NN AO provids a highly promising solution for the future development of satellite quantum communication.
Quantum Key Distribution (QKD) promises a provably secure method to transmit information from one party to another. Free-space QKD allows for this information to be sent over great distances and in places where fibre-based communications cannot be implemented, such as ground-satellite. The primary limiting factor for free-space links is the effect of atmospheric turbulence, which can result in significant error rates and increased losses in QKD channels. Here, we employ the use of a high-speed Adaptive Optics (AO) system to make real-time corrections to the wavefront distortions on spatial modes that are used for high-dimensional QKD in our turbulent channel. First, we demonstrate the effectiveness of the AO system in improving the coupling efficiency of a Gaussian mode that has propagated through turbulence. Through process tomography, we show that our system is capable of significantly reducing the crosstalk of spatial modes in the channel. Finally, we show that employing AO reduces the quantum dit error rate for a high-dimensional orbital angular momentum-based QKD protocol, allowing for secure communication in a channel where it would otherwise be impossible. These results are promising for establishing long-distance free-space QKD systems. High-dimensional quantum key distribution will allow for higher information density and greater error tolerance in future quantum networks. This work experimentally demonstrates how implementing an adaptive optics system in a spatial-mode free-space optical link can allow for quantum communications where it would otherwise be impossible.
A high-dimensional quantum key distribution (QKD), which adopts degrees of freedom of the orbital angular momentum (OAM) states, is beneficial to realize secure and high-speed QKD. However, the helical phase of a vortex beam that carries OAM is sensitive to the atmospheric turbulence and easily distorted. In this paper, an adaptive compensation method using deep learning technology is developed to improve the performance of OAM-encoded QKD schemes. A convolutional neural network model is first trained to learn the mapping relationship of intensity profiles of inputs and the turbulent phase, and such mapping is used as feedback to control a spatial light modulator to generate a phase screen to correct the distorted vortex beam. Then an OAM-encoded QKD scheme with the capability of real-time phase correction is designed, in which the compensation module only needs to extract the intensity distributions of the Gaussian probe beam and thus ensures that the information encoded on OAM states would not be eavesdropped. The results show that our method can efficiently improve the mode purity of the encoded OAM states and extend the secure distance for the involved QKD protocols in the free-space channel, which is not limited to any specific QKD protocol.
Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. We show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application.
Future quantum networks will be composed of both terrestrial links for metropolitan and continent-scale connections and space-based links for global coverage and infrastructure resilience. However, the propagation of quantum signals through the atmosphere is severely impacted by the effects of turbulence. This is even more the case for entanglement-based quantum communication protocols requiring two free-space channels to be considered simultaneously. In this work, we assess the advantage of turbulence mitigation by adaptive optics, in particular during daytime link operation, so as to increase the coupling of the received signal into an optical fiber. We show in particular that this improves the performance of entanglement-based quantum key distribution by up to a few hundred bits per second when compared with the uncorrected scenario