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Tyler Besselman

Publications and source records attributed to Tyler Besselman.

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Tight Quantum Time-Space Tradeoffs for Permutation Inversion

In permutation inversion, we are given a permutation $\pi : [N] \rightarrow [N]$, and want to prepare some advice of size $S$, such that we can efficiently invert any image in time $T$. This is a fundamental cryptographic problem with profound connections to communication complexity and circuit lower bounds. In the classical setting, a tight $ST = \tilde{\Theta}(N)$ bound has been established since the seminal work of Hellman (1980) and Yao (1990). In the quantum setting, a lower bound of $ST^2 = \tilde{\Omega}(N)$ is proved by Nayebi, Aaronson, Belovs, and Trevisan (2015) against classical advice, and by Hhan, Xagawa and Yamakawa (2019) against quantum advice. It left open an intriguing possibility that Grover's search can be sped up to time $\tilde{O}(\sqrt{N / S})$. In this work, we prove an $ST + T^2 = \Omega(N)$ lower bound for permutation inversion with even quantum advice. This bound matches the best known attacks and shows that Grover's search and the classical Hellman's algorithm cannot be further sped up. Our proof combines recent techniques by Liu (2023) and by Rosmanis (2022). Specifically, we first reduce the permutation inversion problem against quantum advice to a variant by Liu's technique, then we analyze this variant via representation theory inspired by Rosmanis (2022).

cs.CC

Direct Sums for Parity Decision Trees

Direct sum theorems state that the cost of solving $k$ instances of a problem is at least $\Omega(k)$ times the cost of solving a single instance. We prove the first such results in the randomised parity decision tree model. We show that a direct sum theorem holds whenever (1) the lower bound for parity decision trees is proved using the discrepancy method; or (2) the lower bound is proved relative to a product distribution.

cs.CC