The paper shows Montanaro's quantum backtracking algorithm can be implemented with O(n log d) data qubits and an O(log m) predicate counter.
Quantum speedup of branch-and-bound algorithms
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abstract
Branch-and-bound is a widely used technique for solving combinatorial optimisation problems where one has access to two procedures: a branching procedure that splits a set of potential solutions into subsets, and a cost procedure that determines a lower bound on the cost of any solution in a given subset. Here we describe a quantum algorithm that can accelerate classical branch-and-bound algorithms near-quadratically in a very general setting. We show that the quantum algorithm can find exact ground states for most instances of the Sherrington-Kirkpatrick model in time $O(2^{0.226n})$, which is substantially more efficient than Grover's algorithm.
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Practical implementation of a quantum backtracking algorithm
The paper shows Montanaro's quantum backtracking algorithm can be implemented with O(n log d) data qubits and an O(log m) predicate counter.