%0 Generic %1 bapst2019pattern %A Bapst, Frederic %A Bhimji, Wahid %A Calafiura, Paolo %A Gray, Heather %A Lavrijsen, Wim %A Linder, Lucy %D 2019 %K quantumcomputing %T A pattern recognition algorithm for quantum annealers %U http://arxiv.org/abs/1902.08324 %X The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a Quadratic Unconstrained Binary Optimization (QUBO) using software and quantum annealing. At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

@misc{bapst2019pattern, abstract = {The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a Quadratic Unconstrained Binary Optimization (QUBO) using software and quantum annealing. At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.}, added-at = {2023-02-08T15:22:29.000+0100}, author = {Bapst, Frederic and Bhimji, Wahid and Calafiura, Paolo and Gray, Heather and Lavrijsen, Wim and Linder, Lucy}, biburl = {https://www.bibsonomy.org/bibtex/2d4e71cb58c5595bb8373561b427b92e9/cmcneile}, description = {A pattern recognition algorithm for quantum annealers}, interhash = {bab8926135dd845652deea3b78154594}, intrahash = {d4e71cb58c5595bb8373561b427b92e9}, keywords = {quantumcomputing}, note = {cite arxiv:1902.08324}, timestamp = {2023-02-08T15:22:29.000+0100}, title = {A pattern recognition algorithm for quantum annealers}, url = {http://arxiv.org/abs/1902.08324}, year = 2019 }