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University of Maryland
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Maximum degree in tournaments is provably $螛(n^{3/2})$ for quantum algorithms, matching a long-standing conjecture.
Quantum learning algorithms can achieve cubic separations over classical randomized learners, shattering the longstanding belief that exact learning speedups are constrained by Grover search and Bernstein-Vazirani limits.
Worst-case complexity heavily masks practical quantum utility: applying smoothed analysis unlocks exponential quantum-classical separations on total functions and superpolynomial speedups for foundational string problems.
