Quantifying Grover speed-ups beyond asymptotic analysis
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The usual method for studying run-times of quantum algorithms is via an asymptotic, worst-case analysis. Whilst useful, such a comparison can often fall short: it is not uncommon for algorithms with a large worst-case run-time to end up performing well on instances of practical interest. To remedy this it is necessary to resort to run-time analyses of a more empirical nature, which for sufficiently small input sizes can be performed on a quantum device or a simulation thereof. For larger input sizes, alternative approaches are required. In this paper we consider an approach that combines classical emulation with rigorous complexity bounds: simulating quantum algorithms by running classical versions of the sub-routines, whilst simultaneously collecting information about what the run-time of the quantum routine would have been if it were run instead. To do this accurately and efficiently for very large input sizes, we describe an estimation procedure that provides provable guarantees on the estimates that it obtains. A nice feature of this approach is that it allows one to compare the performance of quantum and classical algorithms on particular inputs of interest, rather than only on those that allow for an easier mathematical analysis. We apply our method to some simple quantum speedups of classical heuristic algorithms for solving the well-studied MAX-k-SAT optimization problem. To do this we first obtain some rigorous bounds (including all constants) on the expected- and worst-case complexities of two important quantum sub-routines, which improve upon existing results and might be of broader interest: Grover search with an unknown number of marked items, and quantum maximum-finding. Our results suggest that such an approach can provide insightful and meaningful information, in particular when the speedup is of a small polynomial nature.
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