Berlin's AIDAQ summit shows AI is now part of the quantum story

[Tundra]

Bitkom's AI, Data and Quantum Summit in Berlin drew more than 2,500 participants this year, up from about 2,135 in 2025, The Quantum Insider reports. There were 220 speakers and 140 sessions across 22 and 23 September. The main theme was the growing overlap between AI and quantum computing. A few years ago these were mostly separate conversations

Nvidia's Dr Kevin Mato spoke about scaling quantum computing with AI supercomputing. One striking presentation came from Bundesdruckerei, Germany's federal printing office, where Dr Wadim Wormsbecher presented work on "AI swarms that halved the quantum resources needed to break ECDSA." ECDSA is the signature scheme used by Bitcoin and many other systems. If AI keeps finding ways to make quantum attacks cheaper, Q-Day estimates could keep falling

The summit also showed how deep Germany's quantum ecosystem has become. Research institutions such as Jülich, DLR and the Fraunhofer institutes were there, along with hardware companies including IQM, eleQtron, planqc, neQxt and kiutra, and software firms like Kipu Quantum and d-fine. Dr Alexandra-Gwyn Paetz said policymakers want "a strong ecosystem, not just individual companies"

Big industrial names presented use cases, including Deutsche Bundesbank, Lufthansa Industry Solutions, BMW, Airbus, TRUMPF, E.ON and SAP, across finance, manufacturing, energy and logistics. Some panels questioned how useful quantum computing is today, which is a healthy sign at an industry event. Combined with today's news about Germany's 640 million euro hardware competition, it is clear Germany is serious about quantum. AIDAQ 2027 is planned for 21 and 22 September

Is AI making quantum attacks on encryption more likely, sooner? And is Germany now Europe's quantum leader, or does the UK still have the edge?

AIDAQ 2026: AI is Now a staple of The Quantum storythequantuminsider.com

[DecentBloke11]

AI and quantum converging makes sense. AI needs more compute, and quantum needs better control, calibration and error decoding, which AI can help with. They feed each other. Error decoding in real time is a good example. Neural network decoders are already being tested