Speaker
Description
Visual information can be manipulated in terms of images, usually captured and then processed through a sequence of computational operations. Alternatively, optical systems can perform such operations directly, reducing computational overhead at the cost of stricter design requirements. We discuss this workflow in the context of quantum technologies. First, we introduce a quantum computational algorithm that uses the quantum Fourier transform to discard the high spatial-frequency qubits of an image, downsampling it to a lower resolution. Our method allows us to capture, compress, and communicate visual information (or high-dimensional natural data) even with limited resources [1,2]. Then, we present a quantum optical pattern recognition method for binary classification tasks. Leveraging the Hong-Ou-Mandel effect, this setup is specifically designed to reproduce classical perceptrons and shallow neural networks. Our method classifies patterns without reconstructing their images, encoding the spatial information of the object in the spectrum of a single photon, providing a superexponential speedup over classical methods [3,4].
References
[1] Simone Roncallo, Lorenzo Maccone and Chiara Macchiavello, Quantum JPEG, AVS Quantum Sci. 5, 043803 (2023)
[2] Emanuele Tumbiolo, Simone Roncallo, Chiara Macchiavello and Lorenzo Maccone, Quantum frequency resampling, npj Quantum Inf. 11, 123 (2025)
[3] Simone Roncallo, Angela Rosy Morgillo, Chiara Macchiavello, Lorenzo Maccone and Seth Lloyd, Quantum optical classifier with superexponential speedup, Commun. Phys. 8 147 (2025)
[4] Simone Roncallo, Angela Rosy Morgillo, Seth Lloyd, Chiara Macchiavello and Lorenzo Maccone, Quantum optical shallow networks, arXiv.2507.21036
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | No |