In this episode, host Michael Marchuk speaks with Carina Lainer, a principal at Roland Berger’s Advanced Technology Center, about how quantum technologies and AI are converging. Carina explains that quantum computing’s probabilistic nature is different from AI hallucinations and is unlikely to worsen them, then discusses why ambitious qubit-scaling forecasts fell short due to error-correction limits. They explore where quantum can help AI, such as quantum generative methods, denoising, and quantum linear solvers, while noting most use cases remain proofs of concept and commercial viability is still challenging. Lainer describes infrastructure needs like cryogenic cooling, outlines progress across hardware approaches (including photonic and neutral-atom systems), and highlights major risks: “harvest now, decrypt later” threats to cryptography, increased bias in areas like credit scoring, and geopolitical power imbalances, alongside optimism for pharma, chemicals, sensors, and communications.

What we talked with Carina about:

-Quantum Uncertainty vs AI Hallucinations

-Quantum Speedups and Energy Gains

-Quantum Data Centers and Cloud Access

-Practical Quantum AI Algorithms

-Research vs Real World Value

-Timeline to Million Qubits

-AI Accelerating Quantum Hardware

-Hardware Types and Ecosystem Turning Point

-Cryptography Threat and Decrypt Later

-Bias and Geopolitical Power Risks

-When Traditional Industries Should Invest

LINK to Carina's article: https://www.rolandberger.com/en/Insights/Publications/Quantum-computing-and-AI-A-superpower-in-the-making.html

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