Philip Walther - University of Vienna

Exploiting Quantum Photonics for Quantum Computing and Machine Learning 

Tuesday, 8th of September, 9:00-9:45, room CTE 1

After providing a brief overview of recent advancements in the generation and processing of multi-photon states [1], I will show the potential of photonic quantum machine learning. After presenting a quantum-enhanced reinforcement learning using a tunable integrated processor [2], I will discuss our development of a so-called quantum memristor for single photons [3]. These devices, which can mimic the behavior of neurons and synapses, hold great promise for the realization of quantum neural networks. I will also present how photonic processors can implement quantum-enhanced kernels for machine learning tasks [4].

At the end I will change topic by briefly discussing the flexibility of photonic systems for tasks that require non-standard quantum computer architectures [5]; and potentially update about our ongoing experimental research aiming to explore the interface between quantum mechanics and general relativity by performing high-precision experiments using entangled photon states as probe [6].

Refs:

[1] Science Advances 10, eadj0993 (2024), [2] Nature 591, 229 (2021),  [3] Nature Photonics 16, 318 (2022),  [4] Nature Photonics 19, 1020 (2025), [5] Optica 10, 200 (2023), [6] Science Advances 10, eadov0215 (2024).