One Weird Trick Tightens the Quantum Adversary Bound, Especially for Success Probability Close to $1/2$
Duyal Yolcu · arXiv (Cornell University) · 2023
The textbook adversary bound for function evaluation states that to evaluate a function $f\colon D\to C$ with success probability $\frac{1}{2}+δ$ in the quantum query model, one needs at least $\left( 2δ-\sqrt{1-4δ^2} \right) Adv(f)$ queries, where $Adv(f)$ is the optimal value of a certain optimization problem. For $δ\ll 1$, this only allows for a bound of $Θ\left(δ^2 Adv(f)\right)$ even after a repetition-and-majority-voting argument. In contrast, the polynomial method can sometimes prove a bound that doesn't converge to $0$ as $δ\to 0$. We improve the $δ$-dependent prefactor and achieve a bound of $2δAdv(f)$. The proof idea is to "turn the output condition into an input condition": From an algorithm that transforms perfectly input-independent initial to imperfectly distinguishable final states, we construct one that transforms imperfectly input-independent initial to perfectly distinguishable final states in the same number of queries by projecting onto the "correct" final subspaces and uncomputing. The resulting $δ$-dependent condition on initial Gram matrices, compared to the original algorithm's condition on final Gram matrices, allows deriving the tightened prefactor.