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Most of the 53 peer-reviewed experiments ran as simulations or quantum-inspired programs on ordinary computers. Their authors tested 41 only on toy or reduced datasets. The reviewers found no explicit demonstration of a quantum advantage.
Polynomial-time quantum algorithms for factoring and quantum simulation are backed by complexity theory. Grover search gains are at most quadratic. Optimization and machine learning on ordinary data rely mostly on heuristics, and researchers have matched several claimed speedups classically.
McKinsey built the range from expert interviews and 2035 baselines for seven finance segments. It footnoted the use cases' impact levers as the impact of quantum computing and AI. McKinsey also called its value estimates approximate.
The authors called the properties predictability, typicality, robustness, verifiability, and usefulness. They wrote that Shor's algorithm satisfies their criteria and proved that some advantages cannot be predicted with classical resources alone.
Teams publishing in Science measured quantum learning advantages on quantum data, including an 11.8-order cut in samples. On ordinary datasets, classical models led on four of five in a peer-reviewed benchmark. HSBC and IBM reported gains in a preprint.
