First Photon Machine Learning

• URL: http://arxiv.org/abs/2410.17471v1
• Date: Tue, 22 Oct 2024 23:04:55 GMT
• Title: First Photon Machine Learning
• Authors: Lili Li, Santosh Kumar, Malvika Garikapati, Yu-Ping Huang,
• Abstract summary: We present first photon machine learning as a new paradigm of neural networks. By extending the physics behind the double-slit experiment for quantum particles to a many-slit version, our experiment finds that a single photon can perform image recognition at around $30%$ fidelity.
• Score: 1.4416132811087747
• License:
• Abstract: Quantum techniques are expected to revolutionize how information is acquired, exchanged, and processed. Yet it has been a challenge to realize and measure their values in practical settings. We present first photon machine learning as a new paradigm of neural networks and establish the first unambiguous advantage of quantum effects for artificial intelligence. By extending the physics behind the double-slit experiment for quantum particles to a many-slit version, our experiment finds that a single photon can perform image recognition at around $30\%$ fidelity, which beats by a large margin the theoretical limit of what a similar classical system can possibly achieve (about 24\%). In this experiment, the entire neural network is implemented in sub-attojoule optics and the equivalent per-calculation energy cost is below $10^{-24}$ joule, highlighting the prospects of quantum optical machine learning for unparalleled advantages in speed, capacity, and energy efficiency.

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