Lower Bounds for Function Inversion with Quantum Advice
Kai-Min Chung, Tai-Ning Liao, Luowen Qian

TL;DR
This paper establishes fundamental lower bounds on the resources needed for quantum algorithms to invert functions with quantum advice, extending classical bounds to the quantum setting and introducing a generalized quantum random access code.
Contribution
It proves asymptotic lower bounds for quantum function inversion with quantum advice and generalizes quantum random access codes for correlated variables.
Findings
Quantum lower bounds match classical results for inverting functions.
Generalized quantum random access code bounds are nearly tight.
Results apply to fully quantum algorithms with quantum advice.
Abstract
Function inversion is the problem that given a random function , we want to find pre-image of any image in time . In this work, we revisit this problem under the preprocessing model where we can compute some auxiliary information or advice of size that only depends on but not on . It is a well-studied problem in the classical settings, however, it is not clear how quantum algorithms can solve this task any better besides invoking Grover's algorithm, which does not leverage the power of preprocessing. Nayebi et al. proved a lower bound for quantum algorithms inverting permutations, however, they only consider algorithms with classical advice. Hhan et al. subsequently extended this lower bound to fully quantum algorithms for inverting permutations. In this work, we give the same asymptotic lower bound to fully quantum…
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