Quandela Demonstrates Photonic QPU for Quantum Machine Learning

A European research collaboration demonstrated a photonic quantum reservoir processing platform using Quandela's Belenos QPU, achieving higher-fidelity quantum state tomography than standard benchmarks. The system also performed classical machine learning with ~79.7% accuracy on a 12-mode QPU, advancing hybrid quantum-classical computing.

A research collaboration between Quandela, the Center for Theoretical Physics of the Polish Academy of Sciences, and the University of Warsaw has experimentally demonstrated a quantum reservoir processing QRP platform capable of both classical machine learning and quantum information tasks, supported by the EU Horizon Europe QUONDENSATE Pathfinder project. The system routes single photons from a semiconductor quantum dot through a 12-mode active demultiplexer into Quandela's Belenos QPU 24 modes , with photon-number-resolving PNR detectors at the output. A key result is single-basis quantum state tomography: the QRP protocol reconstructs two-mode density matrices using one fixed unitary transformation, achieving mean fidelity of 0.820 versus 0.747 for a standard PNR benchmark, while also extracting purity, von Neumann entropy, and entanglement negativity. A companion experiment on Quandela's legacy 12-mode Ascella QPU applied hardware-aware perturbation training for classical binary classification, reaching ~79.7% accuracy. The underlying preprint is arXiv:2605.10471 May 2026 , per Quantum Computing Report.