A quantum algorithm for model independent searches for new physics
Konstantin T. Matchev, Prasanth Shyamsundar, Jordan Smolinsky
We propose a novel quantum computing based technique to search for unmodelled anomalies in multi-dimensional binned collider data. We propose to associate an Ising lattice spin site with each bin, with the Ising Hamiltonian suitably constructed from the observed data and a corresponding theoretical expectation. In order to capture spatially correlated anomalies in the data, we introduce spin-spin interactions between neighboring sites, as well as self-interactions. The ground state energy of the resulting Ising Hamiltonian can be used as a new test statistic, which can be computed via adiabatic quantum optimization as implemented, e.g., in D-wave. We demonstrate that our test statistic outperforms some of the most commonly used goodness-of-fit tests. The new approach greatly reduces the look-elsewhere effect by exploiting the typical differences between statistical noise and genuine new physics signals.