For the interested in photonic stuff and/or neural networks: Photonic-native convolutional neural networks. Filipa

Begin forwarded message:

From: Filipa Cavaco Peres Subject: Fwd: QLOC seminar: Wed 23 Apr 14.00 – Daphne Wang Date: 23 April 2025 at 11:50:55 GMT+1 To: fcrperes@onsager.ugr.es

---------- Forwarded message --------- De: Rui Soares Barbosa mailto:rui.soaresbarbosa@inl.int> Date: quarta, 23/04/2025 à(s) 10:15 Subject: Re: QLOC seminar: Wed 23 Apr 14.00 – Daphne Wang To:

Quick reminder of Daphne’s talk this afternoon.

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On 21 Apr 2025, at 14:46, Rui Soares Barbosa mailto:rui.soaresbarbosa@inl.int> wrote:

Hi all,

The next QLOC seminar https://urldefense.com/v3/__https://www.ruisoaresbarbosa.com/qlocseminar__;!... will be on Wednesday 23rd April at 14.00, lasting up to 90 minutes as usual.

We welcome Daphne Wang https://urldefense.com/v3/__https://wangdaphne.github.io/__;!!D9dNQwwGXtA!QP... from Paris-based photonic quantum computing company Quandela, who is visiting this week. She will tell us about photonic-native convolutional neural networks [see details below].

The meeting will be in hybrid format: we’ll meet in person in the Vision Room at INL, but it’s also possible to join online via Zoom.

See you there! rui

If you would like to attend in person but do not have an INL access card, please let me know by 11am on Wednesday so that I can register you as a daily visitor.

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Time: Wednesday, 23rd April 2025 14:00 (WEST) Location: Vision Room Zoom link: https://videoconf-colibri.zoom.us/j/93696244899?pwd=k8ujNzu2UjaSPxI2z8kbctPo... https://urldefense.com/v3/__https://videoconf-colibri.zoom.us/j/93696244899?...

Speaker: Daphne Wang https://urldefense.com/v3/__https://wangdaphne.github.io/__;!!D9dNQwwGXtA!QP... (Quandela)

Title: Photonic-native convolutional neural networks

Abstract

Classical convolutional neural networks have revolutionized the field of machine learning, especially when dealing with images. The key to their success is their inductive bias: their structure mimics the image processing mechanism inside the brain. In the field of quantum machine learning, analogues of such models have been investigated [1,2], each with their pros and cons. In this work, we consider a photonic-native convolutional neural network similar to the one of [2] consisting of passive linear optical circuits and simple adaptative measurements. We then benchmark the performance and resource requirements of our photonic models against their classical counterparts.

[1] Iris Cong, Soonwon Choi, and Mikhail D. Lukin. "Quantum convolutional neural networks." Nature Physics 15: 1273–1278 (2019) https://urldefense.com/v3/__https://doi.org/10.1038/s41567-019-0648-8__;!!D9.... [2] Léo Monbroussou et al. "Subspace preserving quantum convolutional neural network architectures." Quantum Science and Technology 10: 025050 (2024) https://urldefense.com/v3/__https://doi.org/10.1088/2058-9565/adbf43__;!!D9d....

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Note. Information about this and other upcoming meetings (including slides for past talks) can be found at https://www.ruisoaresbarbosa.com/qlocseminar https://urldefense.com/v3/__https://www.ruisoaresbarbosa.com/qlocseminar__;!...

Note. Please let me know if you would like to stop receiving these announcements.

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rui soares barbosa (he/him)

https://www.ruisoaresbarbosa.com/ https://urldefense.com/v3/__https://www.ruisoaresbarbosa.com/__;!!D9dNQwwGXt...