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Optical Fiber Communication Conference

Experimental Demonstration of Quantum Nearest Centroid Classification Algorithm using Orbital Angular Momentum of Photons

Dawei Lyu, Qianke Wang, Jun Liu, Jian Wang

Published Jan 1, 2025• 0 citations
Publisher

Abstract

We experimentally demonstrate a classical simulation of the quantum nearest centroid classification algorithm using orbital angular momentum (OAM) modes of photons, achieving an impressive classification accuracy of 86.7% on the Iris dataset.
DOI
10.1364/ofc.2025.th3i.5

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The generation of light containing large degrees of orbital angular momentum (OAM) has recently been demon- strated in both the classical and quantum regimes. Since there is no fundamental limit to how many quanta of OAM a single photon can carry, optical states with an arbitrarily high difference in this quantum number may, in principle, be entangled. This opens the door to investigations into high-dimensional entanglement shared between states in superpositions of nonzero OAM. Additionally, making use of non-zero OAM states can allow for a dramatic increase in the amount of information carried by a single photon, thus increasing the information capacity of a communication channel. In practice, however, it is difficult to differentiate between states with high OAM numbers with high precision. Here we investigate the ability of deep neural networks to differentiate between states that contain large values of OAM. We show that such networks may be used to differentiate be- tween nearby OAM states that contain realistic amounts of noise, with OAM values of up to 100. Additionally, we examine how the classification accuracy scales with the signal-to-noise ratio of images that are used to train the network, as well as those being tested. Finally, we demonstrate the simultaneous classification of < 100 OAM states with greater than 70 % accuracy. We intend to verify our system with experimentally-produced classi- cal OAM states, as well as investigate possibilities that would allow this technique to work in the few-photon quantum regime.
Quantum random walks have attracted special interest because they could lead to new quantum algorithms. Photons can carry orbital angular momentum (OAM) thereby offering a practical realization of a high-dimensional quantum information carrier. By employing OAM of photons, we experimentally realized the one-dimensional discrete-time quantum random walk. Three steps of a one-dimensional quantum random walk were implemented in our protocol showing the obvious difference between quantum and classical random walks.
Abstract Quantum machine learning (QML) offers a breakthrough by producing atypical patterns that classical systems cannot efficiently produce. This study explores quantum neural networks (QNN) for classifying orbital angular momentum (OAM) modes. A fully quantum model achieving 99.8% accuracy across 10 OAM classes and a hybrid ConvQuantum model reaching accuracy levels of 99.8% under atmospheric turbulence strength (AT) of is developed. The ConvQuantum model significantly reduces prediction time, averaging 0.00025 s per sample, which is essential for real‐time free space optical (FSO) communication. To demonstrate quantum technology's potential in OAM‐FSO communication, a 100 × 100 grayscale image is successfully transmitted, and compared the results with several known models in the field, including feedforward neural networks (FNN), convolutional neural network (CNN), and a vision transformer (ViT) model. The findings indicate that the QNN models outperform classical approaches in calculation time, showcasing the efficiency and enhanced processing power of quantum computing in OAM‐FSO communication.