Abstract
We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with a simplification of recent classical work of Lee, Sidford, and Vempala gives our efficient separation oracle. This in turn implies, via a known algorithm, that O~(n) quantum queries to a membership oracle suffice to implement an optimization oracle (the best known classical upper bound on the number of membership queries is quadratic). We also prove several lower bounds: Ω(n) quantum separation (or membership) queries are needed for optimization if the algorithm knows an interior point of the convex set, and Ω(n) quantum separation queries are needed if it does not.
Highlights
Optimization is a fundamental problem in mathematics and computer science, with many real-world applications
Chakrabarti, Childs, Li, and Wu [CCLW18] discovered a similar upper bound as ours: combining the recent classical work of Lee et al [LSV18] with a quantum algorithm for computing gradients, they show how to implement an optimization oracle via O(n) quantum queries to a membership oracle and to an oracle for the objective function
To the best of our knowledge it is open whether this quadratic classical bound is optimal. √
Summary
Optimization is a fundamental problem in mathematics and computer science, with many real-world applications. Chakrabarti, Childs, Li, and Wu [CCLW18] discovered a similar upper bound as ours: combining the recent classical work of Lee et al [LSV18] with a quantum algorithm for computing gradients, they show how to implement an optimization oracle via O(n) quantum queries to a membership oracle and to an oracle for the objective function. Their proof stays quite close to [LSV18] while ours first simplifies some of the technical lemmas of [LSV18], giving us a slightly simpler presentation and a better error-dependence of the resulting algorithm. They prove several lower bounds that are similar to the ones we prove here
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