Parameter Optimization for Quantum Annealing
Experimental research on the effects of the Lagrangian multiplier, annealing schedule and the embedding on the performance of standard and reverse quantum annealing
M.J. van Loenen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M. Möller – Mentor (TU Delft - Numerical Analysis)
Elena Pulvirenti – Graduation committee member (TU Delft - Applied Probability)
C. Vuik – Mentor (TU Delft - Delft Institute of Applied Mathematics)
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Abstract
Quantum annealing is the continuous transformation of the energy working on a quantum system and then measuring said system. The adiabatic theorem and the Ising model let us leverage quantum annealing to find minimal solutions to quadratic unconstrained binary optimization (QUBO) models by setting the energy present during quantum annealing. In this thesis we investigate the QUBO-dization of the multiple object tracking (MOT) problem and perform parameter optimization on the Lagrangian multiplier, chain strength and annealing time parameters as well as test the effects on quantum annealing performance acquired by adding a pause or quench to the anneal schedule or by performing reverse annealing. Experiments were performed on state of the art quantum annealers developed by D-Wave and MOT problem instances were made by us to provide minimally preprocessed QUBO matrices. Reverse annealing was found to have a better performance than standard anneal schedules and we perceived a relation between the annealing time and quantum annealing performance. During testing we found additional factors that contribute to the performance such as the embedding of the Ising model onto the qubits of the quantum annealer. We combine our findings to provide a full quantum annealing schedule and initial state suitable for quantum annealing on state of the art quantum annealers developed by D-Wave.