Blog over Kunstmatige Intelligentie, Quantum, Deep Learning, Blockchain en Big Data Law

Every technology cycle has its incantation, and quantum computing's is "exponentially faster." The phrase is true just often enough to be dangerous. Quantum algorithms hold a superpolynomial advantage over the best known classical methods for factoring, may deliver substantial advantages on specific quantum-simulation problems, and have provable speedups on a handful of engineered learning problems; none of that establishes a general exponential speedup, and for most workloads the advantage is unproven. This analysis audits the exponential speedup claim against the mathematical results and asks what remains for boards, courts, and regulators once the slogan is stripped away.

Where the proven speedups are, and where they are missing

The scoreboard is more precise than the headlines. Shor's algorithm genuinely threatens public-key cryptography. Grover's algorithm delivers a quadratic gain. Quantum computers are not known to solve NP-complete problems efficiently: the machine does not "try all answers at once," it choreographs interference. We walk through the fine print that separates proven quantum advantage from investor prose, including the caveats attached to famous algorithms for linear algebra and machine learning.

How a bank-backed research collaboration demonstrated certified quantum randomness in 2025

A quantum state evolves deterministically between measurements, but the measurement that ends a computation is probabilistic: the same circuit, run twice, may return different answers, and correctness is a property of distributions. This nondeterministic behavior is physical randomness, distinct from the nondeterministic machines of complexity theory. That is comfortable for machine learning and uncomfortable for verification, audit, and liability. The strangeness is already monetizable. In 2025 a commercial trapped-ion processor produced certifiably random bits, converting physical indeterminacy into a compliance-grade evidentiary object. Meanwhile the reverse direction matured quietly: artificial intelligence now designs error-correction decoders and calibrates quantum hardware, closing a loop in which each field accelerates the other.

What the EU AI Act needs before quantum-AI systems reach the market

The governance stakes do not wait for quantum advantage. The EU AI Act imposes accuracy, robustness, and testing duties without requiring deterministic outputs, and hybrid quantum-classical pipelines will need validation suited to probabilistic behavior. They demand conformity assessment that samples and bounds distributions, plus technical documentation covering two failure surfaces at once, statistical learning and hardware noise. Transparency-first instruments are already the trend in AI regulation, as we analyzed for California's frontier-model disclosure regime in the Daiki SB-53 recipe for frontier AI transparency. Quantum-AI extends that logic to the physics itself.

The full analysis separates the three real speedup regimes, explains barren plateaus and dequantization in plain language, and closes with a concrete agenda for governing quantum technology before the inflection point: distributional testing, dual technical files, and certified entropy standards.