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DTSTAMP:20210518T152201Z
LOCATION:Panorama 1
DTSTART;TZID=Europe/Stockholm:20190619T114500
DTEND;TZID=Europe/Stockholm:20190619T121000
UID:isc_hpc_ISC High Performance 2019_sess223_inv_sp185@linklings.com
SUMMARY:Solving the Binary Paintshop Problem with the Quantum Approximate
Optimisation Algorithm
DESCRIPTION:Focus Session\n\nSolving the Binary Paintshop Problem with the
Quantum Approximate Optimisation Algorithm\n\nLeib\n\nWe investigate the
application of a novel heuristic algorithm for combinatorial optimization
with noisy intermediate scale quantum (NISQ) processors, the Quantum Appro
ximate Optimization Algorithm,(QAOA) to solve an industry relevant problem
, the binary paint shop problem.\nIn the binary painthop problem we striv
e to minimize the number of color changes that are necessary to color a gi
ven sequence of cars, because color changes in the paintshop require expen
sive cleaning procedures. Every car has to be painted twice with two diffe
rent colors, however there is no predefined sequence the colors have to be
applied. This problem is known to be NP-hard, i.e. it is intractable to f
ind the optimal solution, and additionally APX-hard, i.e. it is even diffi
cult to find a good approximation to the problem.\nWe present classically
simulated results of the application of QAOA on the binary paintshop probl
em. QAOA is a hybrid quantum algorithm for NISQ devices that in its origin
al version consists of a parametrized circuit that is optimized with an ou
ter learning loop. We show how to come up with good parameters for the cir
cuits with little to no prior execution of the parametrized circuit on the
quantum processor.\n\nPasses: Conference Pass\n\nTag: Graph Algorithms, P
ost Moore’s Law Computing, Quantum Computing
URL:https://2019.isc-program.com/presentation/?id=inv_sp185&sess=sess223
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