Thrust can be computed in O(N^2) time with a Grover-based quantum algorithm under a sequential data-loading model, and in O(N^2 log N) time classically with sorting, but the quantum advantage is only formal for very restrictive memory models.
Jet analysis by Deterministic Annealing
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abstract
We perform a comparison of two jet clusterization algorithms. The first one is the standard Durham algorithm and the second one is a global optimization scheme, Deterministic Annealing, often used in clusterization problems, and adapted to the problem of jet identification in particle production by high energy collisions; in particular we study hadronic jets in WW production by high energy electron positron scattering. Our results are as follows. First, we find that the two procedures give basically the same output as far as the particle clusterization is concerned. Second, we find that the increase of CPU time with the particle multiplicity is much faster for the Durham jet clustering algorithm in comparison with Deterministic Annealing. Since this result follows from the higher computational complexity of the Durham scheme, it should not depend on the particular process studied here and might be significant for jet physics at LHC as well.
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Quantum Algorithms for Jet Clustering
Thrust can be computed in O(N^2) time with a Grover-based quantum algorithm under a sequential data-loading model, and in O(N^2 log N) time classically with sorting, but the quantum advantage is only formal for very restrictive memory models.